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3D Reconstruction

Artificial Intelligence and Machine Learning (AI/ML)

Information Technology

https://en.wikipedia.org/wiki/3D_reconstruction

In computer vision and computer graphics, 3D reconstruction is the process of capturing the shape and appearance of real objects.
This process can be accomplished either by active or passive methods. If the model is allowed to change its shape in time, this is referred to as non-rigid or spatio-temporal reconstruction.

3D Reconstruction is the process of creating a digital 3D model or representation of a physical object or scene using specialized software and hardware. It is a specialized skill that requires knowledge of computer graphics, photogrammetry, computer vision, and other related fields. 3D reconstruction can be used for a variety of applications, including virtual reality, forensic investigations, architecture, and more. It requires a high level of attention to detail and accuracy to produce a realistic and accurate representation of the original object or scene.

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Artificial Intelligence and Machine Learning (AI/ML)

Information Technology

AI Copywriting involves the utilization of artificial intelligence algorithms to generate written content, including but not limited to advertising copy, articles, and marketing materials. This skill employs machine learning models trained on vast datasets to understand language patterns and produce coherent, contextually relevant text. It is employed to automate and expedite the content creation process, providing businesses with efficient and consistent textual output while maintaining a focus on the specific requirements and objectives of the given task.

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Artificial Intelligence and Machine Learning (AI/ML)

Information Technology

https://en.wikipedia.org/wiki/Artificial_intelligence

Artificial intelligence (AI) is intelligence—perceiving, synthesizing, and inferring information—demonstrated by machines, as opposed to intelligence displayed by non-human animals and humans. Example tasks in which this is done include speech recognition, computer vision, translation between (natural) languages, as well as other mappings of inputs.

AI/ML inference is the process of using machine learning models to make predictions on new data. It involves deploying trained models to production environments and optimizing them for efficiency and accuracy. Inference is a specialized skill that requires expertise in both data science and software engineering. It is a crucial part of many AI/ML applications, including image and speech recognition, natural language processing, and predictive analytics.

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AIOps (Artificial Intelligence For IT Operations)

Artificial Intelligence and Machine Learning (AI/ML)

Information Technology

https://en.wikipedia.org/wiki/Artificial_Intelligence_for_IT_Operations

Artificial Intelligence for IT Operations (AIOps) is a term coined by Gartner in 2016 as an industry category for machine learning analytics technology that enhances IT operations analytics. AIOps is the acronym of "Artificial Intelligence Operations". Such operation tasks include automation, performance monitoring and event correlations among others.

AIOps is a specialized skill that involves the use of artificial intelligence and machine learning algorithms to automate and optimize IT operations. It enables organizations to improve the reliability and performance of their IT infrastructure while reducing operational costs and improving efficiency. AIOps involves the integration of data analytics, automation, and artificial intelligence to enable IT teams to proactively identify and resolve issues, as well as predict and prevent potential problems before they occur. It is a rapidly growing field, and professionals with expertise in AIOps are highly sought-after.

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AWS Certified Machine Learning Specialty

Artificial Intelligence and Machine Learning (AI/ML)

Information Technology

AWS Certified Machine Learning Specialty is a certification program offered by Amazon Web Services that validates an individual's knowledge and expertise in designing, implementing, deploying, and maintaining machine learning solutions on AWS. It covers various aspects of machine learning, including data pre-processing, feature engineering, model selection and optimization, and deployment strategies. The certification program is designed for individuals who have a strong understanding of AWS services and have experience in applying machine learning models to real-world problems.

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Artificial Intelligence and Machine Learning (AI/ML)

Information Technology

https://en.wikipedia.org/wiki/Amazon_SageMaker

Amazon SageMaker is a cloud machine-learning platform that was launched in November 2017. SageMaker enables developers to create, train, and deploy machine-learning (ML) models in the cloud. SageMaker also enables developers to deploy ML models on embedded systems and edge-devices.

AWS SageMaker is a platform that offers a suite of tools for building, training, and deploying machine learning models at scale. With SageMaker, developers and data scientists can quickly create custom algorithms and models, and then use them to make predictions and decisions in real-time. The platform provides a range of pre-built algorithms for common tasks like image recognition and natural language processing, as well as tools for data preparation, model tuning, and deployment. SageMaker is designed to be highly scalable, and can handle large datasets and complex models with ease. As a specialized skill, proficiency in AWS SageMaker can help developers and data scientists to build more accurate and efficient machine learning models, and to deliver better results to end users.

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Activity Recognition

Artificial Intelligence and Machine Learning (AI/ML)

Information Technology

https://en.wikipedia.org/wiki/Activity_recognition

Activity recognition aims to recognize the actions and goals of one or more agents from a series of observations on the agents' actions and the environmental conditions. Since the 1980s, this research field has captured the attention of several computer science communities due to its strength in providing personalized support for many different applications and its connection to many different fields of study such as medicine, human-computer interaction, or sociology.

Activity recognition is the process of using sensors and data analysis techniques to detect and identify human activities with high accuracy. It is a specialized skill that requires knowledge of various machine learning algorithms, signal processing techniques, and data analysis tools. Activity recognition finds applications in healthcare, sports, smart homes, and wearable technologies.

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AdaBoost (Adaptive Boosting)

Artificial Intelligence and Machine Learning (AI/ML)

Information Technology

https://en.wikipedia.org/wiki/AdaBoost

AdaBoost, short for Adaptive Boosting, is a machine learning meta-algorithm formulated by Yoav Freund and Robert Schapire, who won the 2003 Gödel Prize for their work. It can be used in conjunction with many other types of learning algorithms to improve performance. The output of the other learning algorithms is combined into a weighted sum that represents the final output of the boosted classifier. AdaBoost is adaptive in the sense that subsequent weak learners are tweaked in favor of those instances misclassified by previous classifiers. In some problems it can be less susceptible to the overfitting problem than other learning algorithms. The individual learners can be weak, but as long as the performance of each one is slightly better than random guessing, the final model can be proven to converge to a strong learner.

AdaBoost is a machine learning algorithm that combines multiple weak or simple models to create a stronger model, improving the accuracy and robustness of classification or regression tasks. It assigns higher weightage to wrongly classified examples, making it more adaptive to the data and achieving better generalization.

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Adversarial Machine Learning

Artificial Intelligence and Machine Learning (AI/ML)

Information Technology

https://en.wikipedia.org/wiki/Adversarial_machine_learning

Adversarial machine learning is a machine learning technique that attempts to fool models by supplying deceptive input. The most common reason is to cause a malfunction in a machine learning model.

Adversarial Machine Learning is a specialized field focused on developing algorithms and techniques that can identify and defend against attacks on machine learning models. It involves identifying potential vulnerabilities and designing defenses that can protect against attacks from malicious actors attempting to manipulate or deceive the model. This requires a thorough understanding of both machine learning and security principles, making it a highly specialized and technical skill.

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Artificial Intelligence and Machine Learning (AI/ML)

Information Technology

Amazon Alexa is a voice-controlled virtual assistant software developed by Amazon. It is able to perform a variety of tasks including playing music, setting alarms, answering questions, controlling smart home devices and more, all through voice commands. Alexa works in conjunction with Amazon Echo devices or other compatible smart home devices. Specialized Skills are custom commands that can be added to Alexa to provide additional abilities and functionality beyond its basic capabilities.

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Artificial Intelligence and Machine Learning (AI/ML)

Information Technology

Amazon Textract is a machine learning-based OCR (optical character recognition) software that allows users to extract data and text from a variety of document types, including PDFs and images, and then analyze and use it in other applications. It provides a cost-effective and efficient way to digitize and organize large volumes of paper-based documents, ultimately saving time and resources for businesses. A specialized skill in Amazon Textract involves understanding how to use this software to extract and process data accurately and efficiently.

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Artificial Intelligence and Machine Learning (AI/ML)

Information Technology

https://en.wikipedia.org/wiki/Apache_MXNet

Apache MXNet is an open-source deep learning software framework, used to train, and deploy deep neural networks. It is scalable, allowing for fast model training, and supports a flexible programming model and multiple programming languages

Apache MXNet is an open-source deep learning framework designed for scalable and efficient training and deployment of neural networks. It provides a flexible and intuitive interface for building and running various types of neural networks, including convolutional neural networks, recurrent neural networks, and more. MXNet is highly optimized for both CPU and GPU computing, allowing for fast and efficient training on a wide range of hardware platforms. Additionally, it offers support for multiple languages, including Python, R, and Julia, making it a versatile and accessible tool for machine learning practitioners.

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Artificial Intelligence and Machine Learning (AI/ML)

Information Technology

https://en.wikipedia.org/wiki/Apache_Mahout

Apache Mahout is a project of the Apache Software Foundation to produce free implementations of distributed or otherwise scalable machine learning algorithms focused primarily on linear algebra. In the past, many of the implementations use the Apache Hadoop platform, however today it is primarily focused on Apache Spark. Mahout also provides Java/Scala libraries for common maths operations and primitive Java collections. Mahout is a work in progress; a number of algorithms have been implemented.

Apache Mahout is an open-source, scalable machine learning and data mining library that runs on top of Apache Hadoop. It provides a wide range of algorithms for clustering, classification, and collaborative filtering, making it useful for recommendation engines, fraud detection, and more. With Mahout, data scientists and developers can build intelligent systems that can learn from data and make more accurate predictions.

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Artificial Intelligence and Machine Learning (AI/ML)

Information Technology

https://en.wikipedia.org/wiki/Apache_SINGA

Apache SINGA is an Apache top-level project for developing an open source machine learning library. It provides a flexible architecture for scalable distributed training, is extensible to run over a wide range of hardware, and has a focus on health-care applications.

Apache SINGA is an open-source deep learning platform designed for distributed training of large-scale machine learning models. It supports various types of neural networks, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and more. It provides a user-friendly interface for developers to develop, train, and deploy their machine learning models in a distributed environment. Apache SINGA is a specialized skill that requires expertise in deep learning, distributed computing, and programming languages such as Python and C++.

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Applications Of Artificial Intelligence

Artificial Intelligence and Machine Learning (AI/ML)

Information Technology

https://en.wikipedia.org/wiki/Applications_of_artificial_intelligence

Artificial intelligence, defined as intelligence exhibited by machines, has many applications in today's society. More specifically, it is Weak AI, the form of AI where programs are developed to perform specific tasks, that is being utilized for a wide range of activities including medical diagnosis, electronic trading platforms, robot control, and remote sensing. AI has been used to develop and advance numerous fields and industries, including finance, healthcare, education, transportation, and more.

Applications of Artificial Intelligence is a specialized skill that involves developing, designing, and implementing intelligent systems that can perform tasks that typically require human intelligence, such as learning, reasoning, problem-solving, and decision-making. It is a field that has gained significant traction in recent years and has numerous applications across various industries, including healthcare, finance, education, transportation, and more. To acquire this skill, individuals need to have a strong foundation in computer science, mathematics, and data analysis.

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Artificial Intelligence

Artificial Intelligence and Machine Learning (AI/ML)

Information Technology

https://en.wikipedia.org/wiki/Artificial_intelligence

Artificial intelligence (AI) is intelligence demonstrated by machines, unlike the natural intelligence displayed by humans and animals, which involves consciousness and emotionality. The distinction between the former and the latter categories is often revealed by the acronym chosen. 'Strong' AI is usually labelled as AGI while attempts to emulate 'natural' intelligence have been called ABI. Leading AI textbooks define the field as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals. Colloquially, the term "artificial intelligence" is often used to describe machines that mimic "cognitive" functions that humans associate with the human mind, such as "learning" and "problem solving".

Artificial Intelligence (AI) is a specialized skill that involves creating intelligent machines that can simulate human intelligence and perform tasks that typically require human intelligence, such as learning, problem-solving, decision making, and language understanding. AI involves various techniques, including machine learning, deep learning, natural language processing, and robotics. With advancements in technology and AI, it has become an integral part of various industries, including finance, healthcare, transportation, and education. Therefore, having AI skills has become crucial for individuals and businesses to remain competitive.

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Artificial Intelligence Development

Artificial Intelligence and Machine Learning (AI/ML)

Information Technology

https://en.wikipedia.org/wiki/Artificial_intelligence

Artificial intelligence (AI) is intelligence demonstrated by machines, as opposed to the natural intelligence displayed by animals including humans. AI research has been defined as the field of study of intelligent agents, which refers to any system that perceives its environment and takes actions that maximize its chance of achieving its goals.

Artificial Intelligence development is a technical skill that involves the creation of intelligent systems that can learn, reason, perceive, and respond like humans. It requires a deep understanding of computer programming, data analytics, machine learning, neural networks, and other related fields. AI developers work to develop AI algorithms and applications that can enhance efficiency, accuracy, and decision-making across various industries including healthcare, finance, transportation, and many more. It is a specialized skill that is in high demand and requires continuous learning and development to stay competitive.

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Artificial Intelligence Markup Language (AIML)

Artificial Intelligence and Machine Learning (AI/ML)

Information Technology

https://en.wikipedia.org/wiki/Artificial_Intelligence_Markup_Language

AIML, or Artificial Intelligence Markup Language, is an XML dialect for creating natural language software agents.

AIML is a specialized programming language used for creating chatbots, virtual assistants, and other conversational AI applications. It uses predefined patterns and rules to simulate human-like conversations and provides a natural language interface for users to interact with machines. AIML is based on XML and requires expertise in natural language processing and machine learning.

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Artificial Intelligence Risk

Artificial Intelligence and Machine Learning (AI/ML)

Information Technology

Artificial Intelligence Risk involves assessing potential hazards and vulnerabilities associated with the deployment and use of artificial intelligence systems. It encompasses identifying, analyzing, and mitigating risks related to data privacy, security breaches, bias and fairness issues, and unintended consequences of AI algorithms. Organizations utilize Artificial Intelligence Risk methodologies to proactively manage risks, ensure compliance with regulations, and safeguard against reputational damage and financial losses stemming from AI-related incidents.

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Artificial Intelligence Systems

Artificial Intelligence and Machine Learning (AI/ML)

Information Technology

https://en.wikipedia.org/wiki/Artificial_intelligence_systems_integration

The core idea of Artificial Intelligence systems integration is making individual software components, such as speech synthesizers, interoperable with other components, such as common sense knowledgebases, in order to create larger, broader and more capable A.I. systems. The main methods that have been proposed for integration are message routing, or communication protocols that the software components use to communicate with each other, often through a middleware blackboard system.

Artificial Intelligence Systems refers to the creation of intelligent software systems that can perform tasks that typically require human intelligence, such as understanding natural language, recognizing images, and making decisions. This field involves the use of machine learning, natural language processing, and other technologies to build systems that can learn and improve over time. AI systems are being used in a wide range of applications, from virtual assistants to self-driving cars, and are helping to transform many industries. Skilled professionals in AI systems typically have expertise in programming, data analysis, and mathematics.

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Artificial Neural Networks

Artificial Intelligence and Machine Learning (AI/ML)

Information Technology

https://en.wikipedia.org/wiki/Artificial_neural_network

Artificial neural networks (ANNs), usually simply called neural networks (NNs), are computing systems vaguely inspired by the biological neural networks that constitute animal brains.

Artificial Neural Networks (ANN) is a specialized skill that involves designing and training computers to learn and recognize patterns, similar to the way the human brain works. It involves building complex algorithms that can perform tasks such as image recognition, natural language processing, and predictive modeling. ANN is widely used in industries such as finance, healthcare, and marketing to analyze data and make predictions, and it requires a strong background in computer science, mathematics, and statistics.

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Association Rule Learning

Artificial Intelligence and Machine Learning (AI/ML)

Information Technology

https://en.wikipedia.org/wiki/Association_rule_learning

Association rule learning is a rule-based machine learning method for discovering interesting relations between variables in large databases. It is intended to identify strong rules discovered in databases using some measures of interestingness.

Association Rule Learning is a subset of machine learning focused on discovering relationships between variables in a dataset. It involves identifying patterns and correlations, and creating rules that describe how these variables are related. This skill is particularly useful in business intelligence and market basket analysis, where it can be used to identify which products customers buy together, and which products are commonly purchased alone. Association Rule Learning can be applied to a wide range of datasets, but requires specialized knowledge and expertise to be used effectively.

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Attention Mechanisms

Artificial Intelligence and Machine Learning (AI/ML)

Information Technology

https://en.wikipedia.org/wiki/Attention_(machine_learning)

In artificial neural networks, attention is a technique that is meant to mimic cognitive attention. The effect enhances some parts of the input data while diminishing other parts — the motivation being that the network should devote more focus to the important parts of the data, even though they may be small. Learning which part of the data is more important than another depends on the context, and this is trained by gradient descent.

Attention Mechanisms is a skill in the field of artificial intelligence and machine learning. It involves the ability to design and implement models that can focus on specific aspects of complex data inputs, improving the performance of neural networks. This skill requires a deep understanding of algorithms, data structures, and programming languages. Proficiency in Attention Mechanisms also includes the ability to troubleshoot and optimize these models for better accuracy and efficiency.

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Artificial Intelligence and Machine Learning (AI/ML)

Information Technology

https://en.wikipedia.org/wiki/Autoencoder

An autoencoder is a type of artificial neural network used to learn efficient data codings in an unsupervised manner. The aim of an autoencoder is to learn a representation (encoding) for a set of data, typically for dimensionality reduction, by training the network to ignore signal “noise”. Along with the reduction side, a reconstructing side is learned, where the autoencoder tries to generate from the reduced encoding a representation as close as possible to its original input, hence its name. Variants exist, aiming to force the learned representations to assume useful properties. Examples are regularized autoencoders, which are effective in learning representations for subsequent classification tasks, and Variational autoencoders, with applications as generative models. Autoencoders are applied to many problems, from facial recognition to acquiring the semantic meaning of words.

Autoencoders are a type of neural network-based algorithm used in unsupervised machine learning. They are designed to learn how to encode and decode inputs, effectively compressing raw data into a lower-dimensional representation and then reconstructing it. Autoencoders can be used for tasks like image and sound processing, anomaly detection, and data compression.

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Automated Machine Learning

Artificial Intelligence and Machine Learning (AI/ML)

Information Technology

https://en.wikipedia.org/wiki/Automated_machine_learning

Automated machine learning (AutoML) is the process of automating the tasks of applying machine learning to real-world problems. AutoML covers the complete pipeline from the raw dataset to the deployable machine learning model. AutoML was proposed as an artificial intelligence-based solution to the ever-growing challenge of applying machine learning. The high degree of automation in AutoML allows non-experts to make use of machine learning models and techniques without requiring them to become experts in machine learning. Automating the process of applying machine learning end-to-end additionally offers the advantages of producing simpler solutions, faster creation of those solutions, and models that often outperform hand-designed models. AutoML has been used to compare the relative importance of each factor in a prediction model.

Automated Machine Learning (AutoML) is a specialized skill that focuses on developing tools and techniques aimed at automating repetitive and time-consuming tasks in machine learning, such as data preprocessing, feature engineering, model selection, and hyperparameter tuning. AutoML enables data scientists and engineers to build machine learning models more efficiently and with less manual intervention, reducing the time and resources required for developing and deploying accurate models. Successful implementation of AutoML requires knowledge of various machine learning algorithms, optimization techniques, programming languages, and software libraries.

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Azure Cognitive Services

Artificial Intelligence and Machine Learning (AI/ML)

Information Technology

https://en.wikipedia.org/wiki/Microsoft_Azure#Machine_learning

Microsoft Azure, commonly referred to as Azure, is a cloud computing service created by Microsoft for building, testing, deploying, and managing applications and services through Microsoft-managed data centers. It provides software as a service (SaaS), platform as a service (PaaS) and infrastructure as a service (IaaS) and supports many different programming languages, tools, and frameworks, including both Microsoft-specific and third-party software and systems.

Azure Cognitive Services is a cloud-based set of APIs and SDKs that enable developers to easily add cognitive features to their applications. This includes natural language processing, speech recognition, computer vision, and machine learning capabilities, allowing applications to interact with their environments and make intelligent decisions. The service is built on Microsoft's artificial intelligence and machine learning technologies, making it simple to integrate these powerful features into any application.

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Azure Machine Learning

Artificial Intelligence and Machine Learning (AI/ML)

Information Technology

https://en.wikipedia.org/wiki/Microsoft_Azure

Microsoft Azure, commonly referred to as Azure, is a cloud computing service created by Microsoft for building, testing, deploying, and managing applications and services through Microsoft-managed data centers. It provides software as a service (SaaS), platform as a service (PaaS) and infrastructure as a service (IaaS) and supports many different programming languages, tools, and frameworks, including both Microsoft-specific and third-party software and systems.

Azure Machine Learning is a cloud-based platform that provides a set of services to build, train, deploy, and manage machine learning models. It includes tools for data preparation, feature engineering, model selection and tuning, and deployment, as well as integration with various programming languages and frameworks. With Azure Machine Learning, users can create complex models and workflows that can be deployed to production environments with ease. The platform also offers various tools and services for monitoring and managing the performance of the deployed models. Overall, Azure Machine Learning is a powerful platform that enables businesses to leverage the power of machine learning to drive innovation and growth.

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Artificial Intelligence and Machine Learning (AI/ML)

Information Technology

https://en.wikipedia.org/wiki/Baidu

Baidu, Inc. is a Chinese multinational technology company specializing in Internet-related services and products and artificial intelligence (AI), headquartered in Beijing's Haidian District. It is one of the largest AI and Internet companies in the world. The holding company of the group is incorporated in the Cayman Islands. Baidu was incorporated in January 2000 by Robin Li and Eric Xu. The Baidu search engine is currently the fourth largest website in the Alexa Internet rankings. Baidu has origins in RankDex, an earlier search engine developed by Robin Li in 1996, before he founded Baidu in 2000.

Baidu is a Chinese technology company that offers a range of software and internet-related services, including search engines, maps, mobile apps, and cloud storage. It is often referred to as the "Google of China" due to its dominance in the country's online search market. Baidu also offers AI and machine learning solutions, such as language processing and computer vision, to business clients.

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Boltzmann Machine

Artificial Intelligence and Machine Learning (AI/ML)

Information Technology

https://en.wikipedia.org/wiki/Boltzmann_machine

A Boltzmann machine is a stochastic spin-glass model with an external field, i.e., a Sherrington–Kirkpatrick model, that is a stochastic Ising model. It is a statistical physics technique applied in the context of cognitive science. It is also classified as a Markov random field.

Boltzmann Machine is a type of artificial neural network skill, used for optimization problems. It involves understanding stochastic, recurrent, and unsupervised learning. Proficiency in Boltzmann Machine indicates the ability to model complex, non-linear relationships, handle large datasets, and perform machine learning tasks such as pattern recognition and data generation. It requires knowledge of statistical mechanics, probability theory, and computational algorithms.

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Artificial Intelligence and Machine Learning (AI/ML)

Information Technology

https://en.wikipedia.org/wiki/Boosting

Boost or boosting may refer to:

Boosting is a specialized skill that involves improving one's performance, status, or rankings in a particular game or activity by receiving help or support from someone who is more skilled or experienced. This can involve playing with or being coached by a higher-ranked player, using cheats or exploits to gain an unfair advantage, or purchasing in-game items or currency to progress more quickly. Boosting is often frowned upon in gaming communities and can result in penalties or bans from gaming platforms. Ethical boosting involves improving one's skills through dedicated practice and hard work.

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Artificial Intelligence and Machine Learning (AI/ML)

Information Technology

Bot Framework is a software development framework provided by Microsoft that allows developers to build, deploy, and manage intelligent bots for various platforms and channels, such as Facebook Messenger, Slack, and Skype, using various programming languages such as C#, Node.js or Python. The framework provides features like natural language processing, dialog management, and integration with various AI services such as LUIS and QnA Maker, making it easier for developers to create unique, conversational experiences.

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Caffe (Framework)

Artificial Intelligence and Machine Learning (AI/ML)

Information Technology

https://en.wikipedia.org/wiki/Caffe_(software)

CAFFE is a deep learning framework, originally developed at University of California, Berkeley. It is open source, under a BSD license. It is written in C++, with a Python interface.

Caffe is an open-source deep learning framework that is used for developing and training powerful machine learning models. It is particularly popular in the computer vision community and is used for tasks such as image and video classification, object detection, and segmentation. Caffe supports a variety of popular neural network architectures and is known for its fast performance and ease of use. It is a specialized skill because it requires expertise in machine learning and computer vision to effectively use and apply Caffe.

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Artificial Intelligence and Machine Learning (AI/ML)

Information Technology

Caffe2 is an open-source deep learning framework used for designing, training, and deploying machine learning models. It is used for various applications such as computer vision, natural language processing and speech recognition. Caffe2 is known for its speed, flexibility and scalability, making it a popular choice for researchers, developers and businesses.

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Artificial Intelligence and Machine Learning (AI/ML)

Information Technology

https://en.wikipedia.org/wiki/ChatGPT

ChatGPT is an artificial intelligence chatbot developed by OpenAI and launched in November 2022. It is built on top of OpenAI's GPT-3 family of large language models and has been fine-tuned using both supervised and reinforcement learning techniques.

ChatGPT is a specialized skill related to the development and optimization of chatbots, which are computer programs designed to simulate human conversation. It involves programming and implementing chatbot functionalities such as natural language processing, machine learning, and understanding user behavior. The goal of ChatGPT is to create chatbots that can provide informative and enjoyable experiences for users, while also optimizing efficiency and effectiveness in customer-service interactions.

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Artificial Intelligence and Machine Learning (AI/ML)

Information Technology

https://en.wikipedia.org/wiki/Chatbot

A chatbot is a software application used to conduct an on-line chat conversation via text or text-to-speech, in lieu of providing direct contact with a live human agent. A chatbot is a type of software that can automate conversations and interact with people through messaging platforms. Designed to convincingly simulate the way a human would behave as a conversational partner, chatbot systems typically require continuous tuning and testing, and many in production remain unable to adequately converse or pass the industry standard Turing test. The term "ChatterBot" was originally coined by Michael Mauldin in 1994 to describe these conversational programs.

Chatbot is a specialized skill in the field of software development that involves creating software programs capable of automatically generating responses to user inquiries through chat or text messaging interfaces. It uses artificial intelligence and natural language processing techniques to understand user queries, interpret them, and provide appropriate responses. Chatbots are widely used in various industries for customer support, marketing, and sales, among others.

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Classification And Regression Tree (CART)

Artificial Intelligence and Machine Learning (AI/ML)

Information Technology

https://en.wikipedia.org/wiki/Classification_and_regression_tree

Decision tree learning is one of the predictive modelling approaches used in statistics, data mining and machine learning. It uses a decision tree to go from observations about an item to conclusions about the item's target value. Tree models where the target variable can take a discrete set of values are called classification trees; in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. Decision trees where the target variable can take continuous values are called regression trees. Decision trees are among the most popular machine learning algorithms given their intelligibility and simplicity.

Classification And Regression Tree (CART) is a decision tree algorithm used for solving classification and regression problems. It is a specialized skill in the field of machine learning that involves building a tree-like model to represent decisions and their possible consequences. CART works by recursively partitioning the data based on the values of different features and finding the best split that minimizes the impurity in the resulting subgroups. The algorithm can be used for a variety of applications, including predicting customer behavior, identifying fraudulent transactions, and analyzing medical data. However, developing a CART model requires expertise in data analysis, statistics, and programming.

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Cognitive Automation

Artificial Intelligence and Machine Learning (AI/ML)

Information Technology

https://en.wikipedia.org/wiki/Automation#Cognitive_automation

Automation describes a wide range of technologies that reduce human intervention in processes. Human intervention is reduced by predetermining decision criteria, subprocess relationships, and related actions — and embodying those predeterminations in machines.

Cognitive automation is a specialized skill that involves using artificial intelligence and machine learning to automate processes that involve high-level thinking and decision-making. This type of automation goes beyond basic robotic automation and can be used to improve accuracy, speed, and efficiency in a variety of industries, such as finance, manufacturing, and healthcare. It requires expertise in data analysis, programming, and algorithm development.

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Cognitive Computing

Artificial Intelligence and Machine Learning (AI/ML)

Information Technology

https://en.wikipedia.org/wiki/Cognitive_computing

Cognitive computing (CC) refers to technology platforms that, broadly speaking, are based on the scientific disciplines of artificial intelligence and signal processing. These platforms encompass machine learning, reasoning, natural language processing, speech recognition and vision, human–computer interaction, dialog and narrative generation, among other technologies.

Cognitive Computing is a specialized field that involves using artificial intelligence and machine learning techniques to enable computers to process and analyze complex human-like thoughts and actions. It requires advanced programming and data analysis skills, as well as a deep understanding of human behavior and psychology. Professionals in this field typically work on developing algorithms, designing systems, and creating applications and tools that can solve complex problems and improve decision-making processes.

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Cognitive Robotics

Artificial Intelligence and Machine Learning (AI/ML)

Information Technology

https://en.wikipedia.org/wiki/Cognitive_robotics

Cognitive robotics is a subfield of robotics concerned with endowing a robot with intelligent behavior by providing it with a processing architecture that will allow it to learn and reason about how to behave in response to complex goals in a complex world. Cognitive robotics may be considered the engineering branch of embodied cognitive science and embodied embedded cognition.

Cognitive Robotics is a specialized skill that involves the combination of cognitive science, artificial intelligence, and robotics to develop intelligent machines that can interact with the environment and humans. It focuses on developing robots that are capable of perceiving, reasoning, learning, and acting intelligently, similar to humans. This field requires expertise in several areas, including computer science, engineering, psychology, neuroscience, and linguistics, among others. The goal of cognitive robotics is to create smart machines that can understand the world and adapt to changing circumstances without human intervention.

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Collaborative Filtering

Artificial Intelligence and Machine Learning (AI/ML)

Information Technology

https://en.wikipedia.org/wiki/Collaborative_filtering

Collaborative filtering (CF) is a technique used by recommender systems. Collaborative filtering has two senses, a narrow one and a more general one.

Collaborative filtering is a type of recommendation system used by businesses to suggest products or services to customers based on the preferences of other customers with similar likes and dislikes. It requires specialized skills such as data analysis, machine learning, and user behavior modeling. This technique can be used in various industries, such as e-commerce, entertainment, and social networking.

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Computational Intelligence

Artificial Intelligence and Machine Learning (AI/ML)

Information Technology

https://en.wikipedia.org/wiki/Computational_intelligence

The expression computational intelligence (CI) usually refers to the ability of a computer to learn a specific task from data or experimental observation. Even though it is commonly considered a synonym of soft computing, there is still no commonly accepted definition of computational intelligence.

Computational Intelligence is a specialized skill that applies the principles of artificial intelligence and machine learning to solve complex problems and make decisions in situations where traditional algorithms and programs may fall short. It involves techniques such as neural networks, fuzzy logic, genetic algorithms, and evolutionary computing to enable systems to learn and adapt to changing inputs and conditions. Computational Intelligence has numerous applications in industries such as finance, healthcare, marketing, and robotics, among others. It is an area of expertise requiring advanced knowledge of statistics, mathematics, programming, and data science.

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Artificial Intelligence and Machine Learning (AI/ML)

Information Technology

https://en.wikipedia.org/wiki/Confusion_matrix

In the field of machine learning and specifically the problem of statistical classification, a confusion matrix, also known as an error matrix, is a specific table layout that allows visualization of the performance of an algorithm, typically a supervised learning one. Each row of the matrix represents the instances in a predicted class, while each column represents the instances in an actual class. The name stems from the fact that it makes it easy to see whether the system is confusing two classes.

A confusion matrix is a table used to evaluate the performance of a machine learning algorithm in classification tasks. It summarizes the number of instances that are correctly classified and misclassified by the model. The confusion matrix displays the true positive, true negative, false positive, and false negative values. It is a useful tool for understanding the accuracy and precision of the model and identifying areas for improvement. Understanding confusion matrix is important for anyone working in the field of data science or machine learning.

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Convolutional Neural Networks

Artificial Intelligence and Machine Learning (AI/ML)

Information Technology

Convolutional Neural Networks (CNNs) are a specialized type of neural network commonly used for image and video recognition and classification tasks. They work by using filters (kernels) to scan through images to identify patterns and features. CNNs are trained on large amounts of image data and are able to recognize objects in new images with high accuracy. Mastering CNNs requires expertise in machine learning, computer vision, and programming.

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Artificial Intelligence and Machine Learning (AI/ML)

Information Technology

https://en.wikipedia.org/wiki/Cortana

Cortana is a virtual assistant developed by Microsoft which uses the Bing search engine to perform tasks such as setting reminders and answering questions for the user.

Cortana is a virtual assistant developed by Microsoft for Windows operating systems, as well as iOS and Android devices. It uses natural language processing and machine learning to provide users with personalized assistance, including creating reminders, setting alarms, searching the internet, and managing calendars. It also integrates with other Microsoft services such as Office 365, Skype, and OneDrive.

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Artificial Intelligence and Machine Learning (AI/ML)

Information Technology

CuDNN (CUDA Deep Neural Network) is a software library developed by Nvidia to accelerate deep learning models. It provides a set of primitives and neural network layers that can be used to build new and different types of deep learning architectures. CuDNN is optimized for Nvidia GPUs that support CUDA, which enables faster training and inference of deep learning models. It is often used in applications such as image and speech recognition, natural language processing, and autonomous vehicles.

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DALL-E Image Generator

Artificial Intelligence and Machine Learning (AI/ML)

Information Technology

https://en.wikipedia.org/wiki/DALL-E

DALL·E, DALL·E 2, and DALL·E 3 are text-to-image models developed by OpenAI using deep learning methodologies to generate digital images from natural language descriptions, called "prompts."

DALL-E Image Generator is an artificial intelligence model developed for image creation. Using a variant of the GPT-3 architecture, it generates unique images based on textual prompts provided by users. This skill is employed in various creative applications, allowing users to conceptualize and generate diverse visual content, from imaginative artwork to unconventional design ideas, simply by describing their vision in text.

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Artificial Intelligence and Machine Learning (AI/ML)

Information Technology

https://en.wikipedia.org/wiki/Dask_(software)

Dask is an open source library for parallel computing written in Python. Originally developed by Matthew Rocklin, Dask is a community project maintained and sponsored by developers and organizations.

Dask is a flexible parallel computing library for analytic computing in Python. It allows for parallel processing of large datasets, spreading computations across multiple cores and nodes in a cluster, and efficient handling of out-of-memory datasets using disk-based computation. Dask can be integrated with other popular Python libraries such as Pandas, NumPy, and Scikit-learn, making it a powerful tool for data science and machine learning applications.

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Artificial Intelligence and Machine Learning (AI/ML)

Information Technology

Deck.gl is a high-performance, WebGL-powered data visualization library developed by Uber. It provides a layered approach to data visualization and enables deep insights into complex data sets. Proficiency in Deck.gl involves understanding its core concepts, such as layers, views, and effects, and being able to create interactive visualizations. It also requires knowledge of JavaScript and WebGL. Mastery of Deck.gl can enhance data analysis, geospatial mapping, and 3D visualization capabilities.

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Artificial Intelligence and Machine Learning (AI/ML)

Information Technology

https://en.wikipedia.org/wiki/Deep_learning

Deep learning is part of a broader family of machine learning methods based on artificial neural networks with representation learning. Learning can be supervised, semi-supervised or unsupervised.

Deep Learning is a branch of Artificial Intelligence that involves the use of neural networks to enable machines to learn from data and make decisions based on that learning. It requires specialized skills in mathematics, programming, and data analysis, making it a highly coveted skill in the tech industry. Deep Learning is used in a variety of applications, including image and speech recognition, natural language processing, and autonomous vehicles.

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Deep Learning Methods

Artificial Intelligence and Machine Learning (AI/ML)

Information Technology

Deep learning methods are a type of artificial intelligence that utilizes complex algorithms and neural networks to analyze and interpret data. This specialized skill involves training machines to learn from large sets of data to identify patterns, make predictions, and perform various tasks. It is utilized in a wide range of fields, including computer vision, natural language processing, robotics, and more. Individuals with expertise in deep learning methods are highly sought after in the job market, particularly in industries such as healthcare, finance, and technology.

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Artificial Intelligence and Machine Learning (AI/ML)

Information Technology

https://en.wikipedia.org/wiki/Deeplearning4j

Eclipse Deeplearning4j is a programming library written in Java for the Java virtual machine (JVM). It is a framework with wide support for deep learning algorithms. Deeplearning4j includes implementations of the restricted Boltzmann machine, deep belief net, deep autoencoder, stacked denoising autoencoder and recursive neural tensor network, word2vec, doc2vec, and GloVe. These algorithms all include distributed parallel versions that integrate with Apache Hadoop and Spark.

Deeplearning4j is an open-source, distributed deep learning framework that allows users to train and deploy neural networks on a variety of hardware, including GPUs and CPUs. It is written in Java and supports multiple programming languages and platforms, making it a versatile tool for building and applying deep learning models. Some of its features include support for convolutional and recurrent neural networks, advanced optimization algorithms, and compatibility with popular libraries such as TensorFlow and Keras.

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Artificial Intelligence and Machine Learning (AI/ML)

Information Technology

https://en.wikipedia.org/wiki/Dialog_systems

A dialogue system, or conversational agent (CA), is a computer system intended to converse with a human. Dialogue systems employed one or more of text, speech, graphics, haptics, gestures, and other modes for communication on both the input and output channel.

Dialog Systems is a specialized skill that involves designing and developing conversational interfaces that allow users to interact with software applications in a more natural and intuitive way through speech or text. These systems rely on natural language processing (NLP) and machine learning (ML) techniques to interpret and respond to user inputs accurately. Dialog system developers need skills in programming, NLP, ML, user experience design, and product development to create effective and engaging conversational interfaces.

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Artificial Intelligence and Machine Learning (AI/ML)

Information Technology

https://en.wikipedia.org/wiki/Dlib

Dlib is a general purpose cross-platform software library written in the programming language C++. Its design is heavily influenced by ideas from design by contract and component-based software engineering. Thus it is, first and foremost, a set of independent software components. It is open-source software released under a Boost Software License.

Dlib is a C++ library that is mainly used for building complex, optimized, and high-performance software applications. The library provides tools for machine learning, data analysis, facial recognition, object detection, and more. Dlib is portable to various software platforms such as Linux, Windows, and Mac, making it a versatile tool for developers. The library also offers well-written and documented code, making it easy to understand and use by developers.

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Embedded Intelligence

Artificial Intelligence and Machine Learning (AI/ML)

Information Technology

https://en.wikipedia.org/wiki/Embedded_intelligence

Embedded intelligence is characterized as the ability of a product, process or service to reflect on its own operational performance, usage load, or environment to enhance the product performance and lifetime, to increase quality or to ensure customer satisfaction. This self-reflection, facilitated by information collected by embedded sensors, processed locally or communicated remotely for processing, must be considered from the earliest design stage.

Embedded Intelligence refers to the integration of intelligent software algorithms and technologies into electronic devices such as smartphones, tablets, and other electronic devices. This specialized skill involves designing and implementing efficient and optimized algorithms that can seamlessly run on devices with limited computing power and memory. It allows electronic devices to adapt to changing environments, learn from data, and make decisions independent of user input. Deep knowledge of programming languages and software development tools is necessary for mastering Embedded Intelligence.

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Ensemble Methods

Artificial Intelligence and Machine Learning (AI/ML)

Information Technology

https://en.wikipedia.org/wiki/Ensemble_learning

In statistics and machine learning, ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone.
Unlike a statistical ensemble in statistical mechanics, which is usually infinite, a machine learning ensemble consists of only a concrete finite set of alternative models, but typically allows for much more flexible structure to exist among those alternatives.

Ensemble methods refer to machine learning techniques that combine multiple models to improve predictive performance. These methods are particularly useful in cases where a single model is inadequate due to overfitting or underfitting. Ensemble methods can be applied to various types of models, including decision trees, neural networks, and support vector machines. Popular techniques include bagging, boosting, and stacking. Mastering ensemble methods requires a deep understanding of statistical techniques, machine learning principles, and programming skills.

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Artificial Intelligence and Machine Learning (AI/ML)

Information Technology

https://en.wikipedia.org/wiki/Ethics_of_artificial_intelligence

The ethics of artificial intelligence is the branch of the ethics of technology specific to artificially intelligent systems. It is sometimes divided into a concern with the moral behavior of humans as they design, make, use and treat artificially intelligent systems, and a concern with the behavior of machines, in machine ethics.

Ethical AI involves the application of ethical principles in the design, development, and deployment of artificial intelligence systems. It includes understanding and addressing bias in AI algorithms, ensuring transparency in AI decision-making processes, and prioritizing user privacy and data security. This skill also encompasses the ability to create AI solutions that promote fairness, accountability, and human-centric values, while complying with relevant laws and regulations.

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Evolutionary Acquisition Of Neural Topologies

Artificial Intelligence and Machine Learning (AI/ML)

Information Technology

https://en.wikipedia.org/wiki/Evolutionary_acquisition_of_neural_topologies

Evolutionary acquisition of neural topologies (EANT/EANT2) is an evolutionary reinforcement learning method that evolves both the topology and weights of artificial neural networks. It is closely related to the works of Angeline et al. and Stanley and Miikkulainen. Like the work of Angeline et al., the method uses a type of parametric mutation that comes from evolution strategies and evolutionary programming, in which adaptive step sizes are used for optimizing the weights of the neural networks. Similar to the work of Stanley (NEAT), the method starts with minimal structures which gain complexity along the evolution path.

Evolutionary Acquisition Of Neural Topologies (EANT) is a specialized skill used in artificial neural network training. It involves evolving the network structure as well as the weight values, resulting in a more efficient network that can perform complex tasks. EANT requires an experienced practitioner who can design and implement the evolutionary algorithm while also understanding the nuances of neural network architecture.

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Evolutionary Programming

Artificial Intelligence and Machine Learning (AI/ML)

Information Technology

https://en.wikipedia.org/wiki/Evolutionary_programming

Evolutionary programming is one of the four major evolutionary algorithm paradigms. It is similar to genetic programming, but the structure of the program to be optimized is fixed, while its numerical parameters are allowed to evolve.

Evolutionary Programming is a problem-solving technique inspired by the process of biological evolution. It involves creating a population of potential solutions and allowing them to evolve through a process of selection, reproduction, and variation. This approach is particularly useful for optimizing complex systems with many variables and nonlinear relationships, such as in engineering, finance, and biology. However, it requires specialized knowledge and expertise in probability theory, optimization algorithms, and computer programming.

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Expectation Maximization Algorithm

Artificial Intelligence and Machine Learning (AI/ML)

Information Technology

https://en.wikipedia.org/wiki/Expectation-maximization_algorithm

In statistics, an expectation–maximization (EM) algorithm is an iterative method to find (local) maximum likelihood or maximum a posteriori (MAP) estimates of parameters in statistical models, where the model depends on unobserved latent variables. The EM iteration alternates between performing an expectation (E) step, which creates a function for the expectation of the log-likelihood evaluated using the current estimate for the parameters, and a maximization (M) step, which computes parameters maximizing the expected log-likelihood found on the E step. These parameter-estimates are then used to determine the distribution of the latent variables in the next E step.

Expectation maximization algorithm (EM) is a statistical method used to estimate parameters of complex models when only incomplete data is available. It is a two-step iterative process where the first step involves estimating missing or latent values, and the second step maximizes the likelihood function to update the parameters. EM is used in fields such as machine learning, computer vision, and bioinformatics to model and analyze data with hidden variables. Mastering EM requires specialized knowledge in statistics and programming.

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Artificial Intelligence and Machine Learning (AI/ML)

Information Technology

https://en.wikipedia.org/wiki/Expert_system

In artificial intelligence, an expert system is a computer system emulating the decision-making ability of a human expert.
Expert systems are designed to solve complex problems by reasoning through bodies of knowledge, represented mainly as if–then rules rather than through conventional procedural code. The first expert systems were created in the 1970s and then proliferated in the 1980s. Expert systems were among the first truly successful forms of artificial intelligence (AI) software.
An expert system is divided into two subsystems: the inference engine and the knowledge base. The knowledge base represents facts and rules. The inference engine applies the rules to the known facts to deduce new facts. Inference engines can also include explanation and debugging abilities.

Expert systems are computer programs that emulate the decision-making ability of a human expert in a specific domain. They use a knowledge base and a set of rules to analyze information and provide recommendations or solutions. Developing and using expert systems requires specialized skills in artificial intelligence and knowledge engineering. These skills are essential for creating effective and efficient expert systems that can provide accurate and relevant guidance in complex domains.

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Explainable AI (XAI)

Artificial Intelligence and Machine Learning (AI/ML)

Information Technology

https://en.wikipedia.org/wiki/Explainable_artificial_intelligence

Explainable AI (XAI), or Interpretable AI, or Explainable Machine Learning (XML), is artificial intelligence (AI) in which humans can understand the decisions or predictions made by the AI. It contrasts with the "black box" concept in machine learning where even its designers cannot explain why an AI arrived at a specific decision. By refining the mental models of users of AI-powered systems and dismantling their misconceptions, XAI promises to help users perform more effectively. XAI may be an implementation of the social right to explanation. XAI is relevant even if there is no legal right or regulatory requirement. For example, XAI can improve the user experience of a product or service by helping end users trust that the AI is making good decisions. This way the aim of XAI is to explain what has been done, what is done right now, what will be done next and unveil the information the actions are based on. These characteristics make it possible (i) to confirm existing knowledge (ii) to challenge existing knowledge and (iii) to generate new assumptions.

Explainable AI (XAI) is a specialized skill that involves developing artificial intelligence (AI) models and algorithms that can be easily understood and interpreted by humans. It aims to create AI systems that can provide transparent and logical explanations for their actions and decisions, making them more trustworthy, reliable, and accountable. XAI has important applications in various fields, including healthcare, finance, and security, where clear and understandable AI decisions are crucial for ensuring ethical and legal compliance.

ESA55C56CF6775C34F77

Artificial Intelligence and Machine Learning (AI/ML)

Information Technology

Fast.ai is an open-source software library that provides a high-level interface for deep learning and artificial intelligence. It includes pre-trained models and resources for computer vision, natural language processing, and tabular data analysis. Its goal is to make deep learning accessible to anyone, regardless of their technical background or expertise. Fast.ai is designed to provide fast training times and achieve state-of-the-art performance on a wide range of tasks, making it a popular tool for researchers, developers, and data scientists.

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Feature Engineering

Artificial Intelligence and Machine Learning (AI/ML)

Information Technology

https://en.wikipedia.org/wiki/Feature_engineering

Feature engineering is the process of using domain knowledge to extract features from raw data. These features can be used to improve the performance of machine learning algorithms. Feature engineering can be considered as applied machine learning itself.

Feature Engineering is the process of selecting and creating the most relevant features from raw data to improve the performance of a machine learning model. It is a specialized skill that requires an understanding of data, domain knowledge, and expertise in data manipulation techniques. Feature engineers use various techniques such as data cleaning, dimensionality reduction, encoding, scaling, and feature transformation to extract meaningful information from raw data. The quality of features determines the success of a machine learning model, and feature engineering is a crucial step in the process of building a robust and accurate model.

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Feature Extraction

Artificial Intelligence and Machine Learning (AI/ML)

Information Technology

https://en.wikipedia.org/wiki/Feature_extraction

In machine learning, pattern recognition, and image processing, feature extraction starts from an initial set of measured data and builds derived values (features) intended to be informative and non-redundant, facilitating the subsequent learning and generalization steps, and in some cases leading to better human interpretations. Feature extraction is related to dimensionality reduction.

Feature extraction is a process of selecting and transforming important and informative parts of data that can be useful for analysis and modeling. It involves identifying relevant features, such as shapes, textures, patterns or colors, and extracting them from raw data. Feature extraction is a specialized skill that requires knowledge of various methods and techniques, such as signal processing, computer vision, and machine learning. It is commonly used in fields such as image and speech recognition, natural language processing, and data mining to improve the accuracy and efficiency of data analysis and decision-making.

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Artificial Intelligence and Machine Learning (AI/ML)

Information Technology

https://en.wikipedia.org/wiki/Feature_learning

In machine learning, feature learning or representation learning is a set of techniques that allows a system to automatically discover the representations needed for feature detection or classification from raw data. This replaces manual feature engineering and allows a machine to both learn the features and use them to perform a specific task.

Feature learning is the process of automatically discovering useful patterns or features in data that can be used to improve a machine learning model's performance. It is a specialized skill that requires expertise in statistical analysis, programming, and mathematics. Feature learning can be a valuable tool in many applications, including computer vision, natural language processing, and speech recognition.

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Feature Selection

Artificial Intelligence and Machine Learning (AI/ML)

Information Technology

https://en.wikipedia.org/wiki/Feature_selection

In machine learning and statistics, feature selection, also known as variable selection, attribute selection or variable subset selection, is the process of selecting a subset of relevant features for use in model construction. Feature selection techniques are used for several reasons:simplification of models to make them easier to interpret by researchers/users,
shorter training times,
to avoid the curse of dimensionality,
enhanced generalization by reducing overfitting

Feature selection is the process of selecting a subset of relevant features from a larger set of features that are available in a dataset. It is a specialized skill that requires domain knowledge, data analysis expertise, and a deep understanding of statistical and machine learning techniques. Feature selection is important because it can improve the performance of machine learning models, reduce the risk of overfitting, and save computation time and resources. Some common techniques for feature selection include correlation analysis, embedded methods, wrapper methods, and dimensionality reduction techniques such as principal component analysis.

KSOZPJQNGDDTEHVHECN0

Artificial Intelligence and Machine Learning (AI/ML)

Information Technology

https://en.wikipedia.org/wiki/Game_ai

In video games, artificial intelligence (AI) is used to generate responsive, adaptive or intelligent behaviors primarily in non-player characters (NPCs) similar to human-like intelligence. Artificial intelligence has been an integral part of video games since their inception in the 1950s. AI in video games is a distinct subfield and differs from academic AI. It serves to improve the game-player experience rather than machine learning or decision making. During the golden age of arcade video games the idea of AI opponents was largely popularized in the form of graduated difficulty levels, distinct movement patterns, and in-game events dependent on the player's input. Modern games often implement existing techniques such as pathfinding and decision trees to guide the actions of NPCs. AI is often used in mechanisms which are not immediately visible to the user, such as data mining and procedural-content generation.

Game AI refers to the use of artificial intelligence techniques and algorithms to create intelligent behavior for non-player characters (NPCs) in video games. This includes computer-controlled characters that can make strategic decisions, perceive and react to their environment, and interact with players in a realistic and dynamic manner. Game AI requires a combination of programming skills, knowledge of machine learning and pattern recognition, and an understanding of game design principles.

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General-Purpose Computing On Graphics Processing Units

Artificial Intelligence and Machine Learning (AI/ML)

Information Technology

https://en.wikipedia.org/wiki/General-purpose_computing_on_graphics_processing_units

General-purpose computing on graphics processing units is the use of a graphics processing unit (GPU), which typically handles computation only for computer graphics, to perform computation in applications traditionally handled by the central processing unit (CPU). The use of multiple video cards in one computer, or large numbers of graphics chips, further parallelizes the already parallel nature of graphics processing. In addition, even a single GPU-CPU framework provides advantages that multiple CPUs on their own do not offer due to the specialization in each chip.

General-Purpose Computing On Graphics Processing Units, also known as GPGPU, is a specialized skill that involves programming highly parallel computer architectures like GPUs to perform non-graphics computation tasks. These tasks require specialized knowledge of parallel programming techniques to optimize the use of these architectures for efficient and fast computations.

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Generative Adversarial Networks

Artificial Intelligence and Machine Learning (AI/ML)

Information Technology

https://en.wikipedia.org/wiki/Generative_adversarial_network

A generative adversarial network (GAN) is a class of machine learning frameworks designed by Ian Goodfellow and his colleagues in June 2014. Two neural networks contest with each other in the form of a zero-sum game, where one agent's gain is another agent's loss.

Generative Adversarial Networks (GANs) is a specialized skill in machine learning that involves the training of two neural networks, one that generates synthetic data and another that discriminates between real and synthetic data. The two networks are trained simultaneously in a competitive manner, where the generator network tries to produce data that fools the discriminator network, while the discriminator network tries to correctly identify real from fake data. This approach has shown promising results in generating realistic images, videos, and even human-like text.

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Generative Artificial Intelligence

Artificial Intelligence and Machine Learning (AI/ML)

Information Technology

https://en.wikipedia.org/wiki/Generative_artificial_intelligence

Generative artificial intelligence or generative AI is a type of artificial intelligence (AI) system capable of generating text, images, or other media in response to prompts. Generative AI models learn the patterns and structure of their input training data, and then generate new data that has similar characteristics.

Generative Artificial Intelligence refers to the ability to design, implement, and manage AI systems that can create content or solutions autonomously. This skill involves understanding and applying machine learning algorithms, deep learning networks, and other AI technologies to generate new data models. Proficiency in programming languages like Python, Java, or R, and familiarity with AI platforms like TensorFlow or PyTorch is often required. It also involves the ability to analyze and interpret complex data, solve problems creatively, and continuously improve AI systems for better output.

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Genetic Algorithm

Artificial Intelligence and Machine Learning (AI/ML)

Information Technology

https://en.wikipedia.org/wiki/Genetic_algorithm

In computer science and operations research, a genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA). Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems by relying on biologically inspired operators such as mutation, crossover and selection.

A genetic algorithm is a method of solving problems inspired by the process of natural selection. It involves a population of candidate solutions that evolve over time through crossover, mutation, and selection, with the goal of finding the best solution to a given problem. It is a specialized skill typically used in areas such as optimization, machine learning, and artificial intelligence.

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Gesture Recognition

Artificial Intelligence and Machine Learning (AI/ML)

Information Technology

https://en.wikipedia.org/wiki/Gesture_recognition

Gesture recognition is a topic in computer science and language technology with the goal of interpreting human gestures via mathematical algorithms. It is a subdiscipline of computer vision. Gestures can originate from any bodily motion or state but commonly originate from the face or hand. Current focuses in the field include emotion recognition from face and hand gesture recognition. Users can use simple gestures to control or interact with devices without physically touching them. Many approaches have been made using cameras and computer vision algorithms to interpret sign language. However, the identification and recognition of posture, gait, proxemics, and human behaviors is also the subject of gesture recognition techniques.
Gesture recognition can be seen as a way for computers to begin to understand human body language, thus building a richer bridge between machines and humans than primitive text user interfaces or even GUIs, which still limit the majority of input to keyboard and mouse and interact naturally without any mechanical devices. Using the concept of gesture recognition, it is possible to point a finger at this point will move accordingly. This could make conventional input on devices such and even redundant.

Gesture recognition is the ability to interpret and understand human movements and gestures, often using computer algorithms and machine learning techniques. It is a specialized skill that requires expertise in computer vision, machine learning, and artificial intelligence. The field of gesture recognition has many applications, including in human-computer interaction, robotics, and virtual reality.

ESB3A211D0C84934574B

Artificial Intelligence and Machine Learning (AI/ML)

Information Technology

Google AutoML is a suite of machine learning tools that allow developers with little to no expertise in artificial intelligence to create custom machine learning models. With AutoML, developers can create models to classify images, recognize speech, and more, without having to build the underlying algorithms or infrastructure from scratch. This reduces the amount of time needed to design and train machine learning models, while still allowing developers to create models that are tailored to their specific needs.

ES10034B3F54D3592090

Artificial Intelligence and Machine Learning (AI/ML)

Information Technology

https://en.wikipedia.org/wiki/Bard_(chatbot)

Bard is a conversational generative artificial intelligence chatbot developed by Google. Initially based on the LaMDA family of large language models (LLMs), it was later upgraded to PaLM and then to Gemini. Bard was developed as a direct response to the meteoric rise of OpenAI's ChatGPT, and was released in a limited capacity in March 2023 to lukewarm responses before expanding to other countries in May.

Google Bard is a web-based tool designed for creating and managing dynamic, interactive dashboards and visualizations. It utilizes a user-friendly interface to enable individuals to represent data in a comprehensible manner without requiring extensive programming skills. Users can leverage Google Bard to construct compelling visual narratives, incorporating charts and graphs, to facilitate data-driven decision-making and communication within various professional contexts.

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Google Cloud ML Engine

Artificial Intelligence and Machine Learning (AI/ML)

Information Technology

Google Cloud ML Engine is a cloud-based machine learning platform that allows developers and data scientists to build and deploy machine learning models at scale. It provides a range of tools and services for data preparation, model training, model evaluation, and deployment. With Google Cloud ML Engine, users can train and deploy machine learning models on large datasets, leverage pre-trained models to accelerate development, and integrate machine learning into their applications with ease.

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Gradient Boosting

Artificial Intelligence and Machine Learning (AI/ML)

Information Technology

https://en.wikipedia.org/wiki/Gradient_boosting

Gradient boosting is a machine learning technique for regression and classification problems, which produces a prediction model in the form of an ensemble of weak prediction models, typically decision trees. When a decision tree is the weak learner, the resulting algorithm is called gradient boosted trees, which usually outperforms random forest. It builds the model in a stage-wise fashion like other boosting methods do, and it generalizes them by allowing optimization of an arbitrary differentiable loss function.

Gradient Boosting is a machine learning technique that involves combining several weak models to create a stronger model. It is a specialized skill that requires knowledge of algorithms, data pre-processing, feature engineering, and hyperparameter tuning. Gradient Boosting can be used for both classification and regression problems and is considered one of the most powerful and accurate machine learning techniques. It is commonly used in applications such as finance, healthcare, and e-commerce for predicting customer behavior, detecting fraud, and optimizing marketing campaigns.

ESA45DA3CA37FB31DBA4

Artificial Intelligence and Machine Learning (AI/ML)

Information Technology

H2O.ai is an open-source software platform that provides AI and machine learning tools for businesses and data scientists. It allows users to build and deploy machine learning models quickly and efficiently, enabling them to analyze large datasets, make predictions, and make data-driven decisions. Its key features include automatic model selection, data visualization, and text analytics. It is highly scalable and can be used on-premise or on the cloud. H2O.ai is used in a variety of industries, including finance, healthcare, retail, and marketing.

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Hidden Markov Model

Artificial Intelligence and Machine Learning (AI/ML)

Information Technology

https://en.wikipedia.org/wiki/Hidden_Markov_model

Hidden Markov Model (HMM) is a statistical Markov model in which the system being modeled is assumed to be a Markov process – call it  – with unobservable ("hidden") states. HMM assumes that there is another process whose behavior "depends" on . The goal is to learn about by observing . HMM stipulates that, for each time instance , the conditional probability distribution of given the history must not depend on .

Hidden Markov Model (HMM) is a statistical model used to model sequences of data that are dependent on some hidden, or unobserved, variable. HMMs have applications in many fields, including speech recognition, handwriting recognition, and bioinformatics. Understanding and implementing HMMs requires knowledge of probability theory, statistics, and computer science.

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Artificial Intelligence and Machine Learning (AI/ML)

Information Technology

https://en.wikipedia.org/wiki/IPsoft_Inc.

Amelia, formerly known as IPsoft, is an American technology company. It primarily focuses on artificial intelligence, cognitive and autonomic solutions for enterprises.
Amelia, an IPsoft Company is the world leader in Enterprise AI.
Its main products are Amelia, a conversational AI platform, and Amelia HyperAutomation Platform, an autonomic framework for IT operations.

IPSoft Amelia is an artificial intelligence platform designed to automate certain business processes, provide customer service, and support IT services. It uses natural language processing and machine learning to understand and respond to user queries in a human-like manner. Amelia can handle simple and complex tasks, make decisions, and learn from previous interactions. It is widely used in industries such as finance, healthcare, and retail.

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Artificial Intelligence and Machine Learning (AI/ML)

Information Technology

https://en.wikipedia.org/wiki/Inference_engine

In the field of artificial intelligence, inference engine is a component of the system that applies logical rules to the knowledge base to deduce new information. The first inference engines were components of expert systems. The typical expert system consisted of a knowledge base and an inference engine. The knowledge base stored facts about the world. The inference engine applies logical rules to the knowledge base and deduced new knowledge. This process would iterate as each new fact in the knowledge base could trigger additional rules in the inference engine. Inference engines work primarily in one of two modes either special rule or facts: forward chaining and backward chaining. Forward chaining starts with the known facts and asserts new facts. Backward chaining starts with goals, and works backward to determine what facts must be asserted so that the goals can be achieved.

An Inference Engine is a key component of an artificial intelligence system that is responsible for reasoning and making logical deductions from knowledge and data in order to solve problems and make decisions. It utilizes a set of rules and algorithms to analyze and interpret data, and can be used in a range of applications such as expert systems, natural language processing, and machine learning. Developing and maintaining an Inference Engine requires specialized skills such as programming, data analysis, and mathematics.

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Artificial Intelligence and Machine Learning (AI/ML)

Information Technology

https://en.wikipedia.org/wiki/Intelligent_agent

In artificial intelligence, an intelligent agent (IA) refers to an autonomous entity which acts, directing its activity towards achieving goals, upon an environment using observation through sensors and consequent actuators. Intelligent agents may also learn or use knowledge to achieve their goals. They may be very simple or very complex. A reflex machine, such as a thermostat, is considered an example of an intelligent agent.

Intelligent Agent is a computer program or software that can perform tasks independently without any human intervention. It is a specialized skill that requires expertise in areas such as machine learning, artificial intelligence, and computer programming. This technology is being used in various industries such as healthcare, finance, manufacturing, and customer service to automate processes, improve efficiency, and provide better services to customers.

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Intelligent Automation

Artificial Intelligence and Machine Learning (AI/ML)

Information Technology

https://en.wikipedia.org/wiki/Intelligent_automation

Intelligent automation (IA), or alternately intelligent process automation, is a software term that refers to a combination of artificial intelligence (AI) and robotic process automation (RPA). Companies use intelligent automation to cut costs and streamline tasks by using artificial-intelligence-powered robotic software to mitigate repetitive tasks. As it accumulates data, the system learns in an effort to improve its efficiency. Intelligent automation applications consist of but are not limited to, pattern analysis, data assembly, and classification. The term is similar to hyperautomation, a concept identified by research group Gartner as being one of the top technology trends of 2020.

Intelligent Automation involves the integration of artificial intelligence and machine learning technologies to automate complex business processes. This skill leverages advanced algorithms and data analysis capabilities to streamline workflows, enhance efficiency, and reduce human intervention in repetitive tasks. By harnessing Intelligent Automation, organizations can achieve higher productivity, accuracy, and scalability in their operations.

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Intelligent Control

Artificial Intelligence and Machine Learning (AI/ML)

Information Technology

https://en.wikipedia.org/wiki/Intelligent_control

Intelligent control is a class of control techniques that use various artificial intelligence computing approaches like neural networks, Bayesian probability, fuzzy logic, machine learning, reinforcement learning, evolutionary computation and genetic algorithms.

Intelligent control is a specialized skill that involves designing and implementing control systems that are capable of adapting to changing environments and achieving optimal performance in complex and uncertain situations. This requires a deep understanding of control theory, machine learning, and artificial intelligence, as well as expertise in programming and systems integration. Intelligent control is used in a wide variety of applications, such as robotics, aerospace, manufacturing, and transportation, and is an important area of research and development in engineering and computer science.

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Intelligent Systems

Artificial Intelligence and Machine Learning (AI/ML)

Information Technology

Intelligent Systems is a specialized skill that involves the development and implementation of algorithms, technologies, and tools that enable machines or computer systems to mimic human behavior and perform tasks that typically require human intelligence, such as learning, decision-making, and problem-solving. This skill requires expertise in various fields such as artificial intelligence, machine learning, natural language processing, and computer vision. Intelligent Systems are increasingly used in various industries, including healthcare, finance, and automotive, to improve efficiency, accuracy, and decision-making.

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Intelligent Virtual Assistant

Artificial Intelligence and Machine Learning (AI/ML)

Information Technology

https://en.wikipedia.org/wiki/Virtual_assistant

An intelligent virtual assistant (IVA) or intelligent personal assistant (IPA) is a software agent that can perform tasks or services for an individual based on commands or questions. The term "chatbot" is sometimes used to refer to virtual assistants generally or specifically accessed by online chat. In some cases, online chat programs are exclusively for entertainment purposes. Some virtual assistants are able to interpret human speech and respond via synthesized voices. Users can ask their assistants questions, control home automation devices and media playback via voice, and manage other basic tasks such as email, to-do lists, and calendars with verbal (spoken?) commands. A similar concept, however with differences, lays under the dialogue systems.

Intelligent Virtual Assistant is a specialized skill that involves developing and implementing computer programs that can interact with humans in a natural language format using artificial intelligence techniques. These programs can perform a range of tasks such as answering customer queries, automating chat support, scheduling appointments, and providing personalized recommendations. Developing this skill requires expertise in programming languages, natural language processing algorithms, machine learning, and cognitive computing.

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Artificial Intelligence and Machine Learning (AI/ML)

Information Technology

https://en.wikipedia.org/wiki/Interactive_kiosk

An interactive kiosk is a computer terminal featuring specialized hardware and software that provides access to information and applications for communication, commerce, entertainment, or education.

Interactive Kiosk refers to the technology that allows users to interact with a computerized system through a self-contained and standalone device. It requires specialized skills in design, programming, and hardware development to create interactive experiences that are intuitive, user-friendly, and engaging. With the rise of automation and touch-based interfaces in various industries, the demand for skilled interactive kiosk developers and designers is increasing.

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K-Nearest Neighbors Algorithm

Artificial Intelligence and Machine Learning (AI/ML)

Information Technology

https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm

In statistics, the k-nearest neighbors algorithm (k-NN) is a non-parametric classification method first developed by Evelyn Fix and Joseph Hodges in 1951, and later expanded by Thomas Cover. It is used for classification and regression. In both cases, the input consists of the k closest training examples in data set. The output depends on whether k-NN is used for classification or regression:In k-NN classification, the output is a class membership. An object is classified by a plurality vote of its neighbors, with the object being assigned to the class most common among its k nearest neighbors. If k = 1, then the object is simply assigned to the class of that single nearest neighbor.In k-NN regression, the output is the property value for the object. This value is the average of the values of k nearest neighbors.

K-Nearest Neighbors (KNN) is a machine learning algorithm used for both classification and regression tasks. It works by finding the K-nearest points in a dataset to a given data point and then using the labels of those nearest points to classify or predict the label of the given data point. The choice of K will affect the accuracy of the algorithm and needs to be chosen carefully. KNN is a simple but effective algorithm that can be used in many different applications. However, it requires a substantial amount of memory and can be slow for large datasets.

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Artificial Intelligence and Machine Learning (AI/ML)

Information Technology

https://en.wikipedia.org/wiki/Kaldi_(software)

Kaldi is an open-source speech recognition toolkit written in C++ for speech recognition and signal processing, freely available under the Apache License v2.0.

Kaldi is a free and open-source toolkit for speech recognition, signal processing, and speaker identification tasks. It is designed to be modular and flexible, allowing researchers and developers to easily customize and extend its capabilities. Kaldi is widely used in academic and industrial research, and has been used to train state-of-the-art speech recognition systems in many languages.

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Keras (Neural Network Library)

Artificial Intelligence and Machine Learning (AI/ML)

Information Technology

https://en.wikipedia.org/wiki/Keras

Keras is an open-source software library that provides a Python interface for artificial neural networks. Keras acts as an interface for the TensorFlow library.

Keras is an open-source neural network library written in Python. It is designed to enable fast experimentation with deep neural networks and supports various types of convolutional, recurrent, and dense networks. Keras is widely used for building and training machine learning models for a variety of applications, including image recognition, text classification, and natural language processing. Its user-friendly API and pre-built models make it easy for developers to build and deploy machine learning models quickly and easily.

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Artificial Intelligence and Machine Learning (AI/ML)

Information Technology

https://en.wikipedia.org/wiki/Kernel_method

In machine learning, kernel machines are a class of algorithms for pattern analysis, whose best known member is the support-vector machine (SVM). The general task of pattern analysis is to find and study general types of relations in datasets. For many algorithms that solve these tasks, the data in raw representation have to be explicitly transformed into feature vector representations via a user-specified feature map: in contrast, kernel methods require only a user-specified kernel, i.e., a similarity function over pairs of data points in raw representation.

Kernel Methods is a specialized skill in machine learning that involves the use of mathematical techniques to transform data into higher dimensions for easier analysis. This transformation is achieved through the use of a kernel function, which measures the similarity between data points. Kernel Methods are commonly used in classification and regression tasks and can be applied to a wide range of domains like natural language processing, image recognition, and bioinformatics. It requires a deep understanding of linear algebra and calculus and is typically used by advanced machine learning practitioners.

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Knowledge Engineering

Artificial Intelligence and Machine Learning (AI/ML)

Information Technology

Knowledge engineering (KE) refers to all technical, scientific and social aspects involved in building, maintaining and using knowledge-based systems.

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Knowledge-Based Configuration

Artificial Intelligence and Machine Learning (AI/ML)

Information Technology

https://en.wikipedia.org/wiki/Knowledge-based_configuration

Knowledge-based configuration, or also referred to as product configuration or product customization, is an activity of customising a product to meet the needs of a particular customer. The product in question may consist of mechanical parts, services, and software. Knowledge-based configuration is a major application area for artificial intelligence (AI), and it is based on modelling of the configurations in a manner that allows the utilisation of AI techniques for searching for a valid configuration to meet the needs of a particular customer.

Knowledge-Based Configuration is the use of reasoning and knowledge representation techniques to automate the process of configuring complex products or services. It relies on a knowledge base containing information about the product, customer requirements, and constraints. The system can then generate a customized solution based on these inputs, making the process faster and more accurate. This technique is used in a variety of industries, including manufacturing, healthcare, and telecommunications.

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Knowledge-Based Systems

Artificial Intelligence and Machine Learning (AI/ML)

Information Technology

https://en.wikipedia.org/wiki/Knowledge-based_systems

A knowledge-based system (KBS) is a computer program that reasons and uses a knowledge base to solve complex problems. The term is broad and refers to many different kinds of systems. The one common theme that unites all knowledge based systems is an attempt to represent knowledge explicitly and a reasoning system that allows it to derive new knowledge. Thus, a knowledge-based system has two distinguishing features: a knowledge base and an inference engine.

Knowledge-based systems (KBS) are computer programs that use artificial intelligence (AI) to solve complex problems by reasoning about knowledge, represented mainly as if-then rules. They are designed to emulate the problem-solving capabilities of a human expert by encoding their knowledge of a particular domain. KBS are used in various fields, including medicine, finance, law, and engineering, to assist decision-making, diagnose problems, interpret data, and provide recommendations. They are built using specialized software tools and programming languages, such as Prolog and CLIPS.

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Artificial Intelligence and Machine Learning (AI/ML)

Information Technology

https://en.wikipedia.org/wiki/Kubeflow

Kubeflow is a free and open-source machine learning platform designed to enable using machine learning pipelines to orchestrate complicated workflows running on Kubernetes. Kubeflow was based on Google's internal method to deploy TensorFlow models called TensorFlow Extended.

Kubeflow is an open-source machine learning platform that is designed to run on Kubernetes, which makes it easier to streamline the deployment, scaling, and management of machine learning workflows. It provides a set of tools and workflows that can help data scientists and developers build, train, and deploy machine learning models in a more efficient manner. Kubeflow also supports a wide range of machine learning frameworks and can be integrated with other tools and services to create end-to-end machine learning pipelines.

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Artificial Intelligence and Machine Learning (AI/ML)

Information Technology

https://en.wikipedia.org/wiki/Language_model

A statistical language model is a probability distribution over sequences of words. Given such a sequence, say of length m, it assigns a probability to the whole sequence.

A language model is a computer program that can understand and generate human language. It is a specialized skill that requires a deep understanding of the nuances of human communication, as well as the ability to process vast amounts of data and identify patterns. Language models are used in a wide range of applications, from chatbots and virtual assistants to automated translation and text analysis. They rely on machine learning algorithms to learn from large datasets and generate more advanced responses over time.

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Large Language Modeling

Artificial Intelligence and Machine Learning (AI/ML)

Information Technology

https://en.wikipedia.org/wiki/Large_language_model

A large language model (LLM) is a language model consisting of a neural network with many parameters, trained on large quantities of unlabeled text using self-supervised learning. LLMs emerged around 2018 and perform well at a wide variety of tasks. This has shifted the focus of natural language processing research away from the previous paradigm of training specialized supervised models for specific tasks.

Large Language Modeling is a skill that involves developing, training, and implementing machine learning models to understand and generate human language. It requires proficiency in natural language processing (NLP), deep learning algorithms, and programming languages like Python. Key tasks include data preprocessing, model training, and performance evaluation. Familiarity with tools such as GPT-3, BERT, and TensorFlow is often necessary. This skill also demands a strong understanding of linguistics and computational semantics to ensure accurate language interpretation.

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Artificial Intelligence and Machine Learning (AI/ML)

Information Technology

https://en.wikipedia.org/wiki/LIBSVM

LIBSVM and LIBLINEAR are two popular open source machine learning libraries, both developed at the National Taiwan University and both written in C++ though with a C API. LIBSVM implements the Sequential minimal optimization (SMO) algorithm for kernelized support vector machines (SVMs), supporting classification and regression.
LIBLINEAR implements linear SVMs and logistic regression models trained using a coordinate descent algorithm.

LIBSVM is a machine learning software library designed for support vector classification, regression, and distribution estimation. It is written in C++ and provides a simple user interface for training and testing SVM models. LIBSVM supports various kernel functions and parameters, making it suitable for a wide range of applications in pattern recognition, image classification, and bioinformatics. Its efficient implementation allows it to handle large datasets with millions of samples and thousands of features.

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Artificial Intelligence and Machine Learning (AI/ML)

Information Technology

https://en.wikipedia.org/wiki/LightGBM

LightGBM, short for light gradient-boosting machine, is a free and open-source distributed gradient-boosting framework for machine learning, originally developed by Microsoft. It is based on decision tree algorithms and used for ranking, classification and other machine learning tasks. The development focus is on performance and scalability.

LightGBM, a gradient boosting framework, is designed for efficient and distributed machine learning tasks. Employing tree-based models, it excels in handling large datasets and high-dimensional features. This skill is utilized for tasks such as classification, regression, and ranking, providing fast and accurate predictions by constructing and optimizing an ensemble of decision trees.

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Long Short-Term Memory (LSTM)

Artificial Intelligence and Machine Learning (AI/ML)

Information Technology

https://en.wikipedia.org/wiki/Long_short-term_memory

Long short-term memory (LSTM) is an artificial recurrent neural network (RNN) architecture used in the field of deep learning. Unlike standard feedforward neural networks, LSTM has feedback connections. It can not only process single data points, but also entire sequences of data. For example, LSTM is applicable to tasks such as unsegmented, connected handwriting recognition, speech recognition and anomaly detection in network traffic or IDSs.

Long Short-Term Memory (LSTM) is a type of artificial neural network that is capable of remembering long-term dependencies between inputs, making it well-suited for tasks such as speech recognition and natural language processing. LSTM networks use a series of gates to control the flow of information through the network, allowing them to selectively remember or forget information over time. This skill is often used in the development of chatbots, machine translation, and other applications that involve processing natural language.