Modelos Secuenciales - NLP - Redes Neuronales - Curso
Redes Neuronales Recurrentes - CURSO
Horario: jueves de 18.30 a 21.30
Comienzo: jueves 23 de mayo
LUGAR: Sede Lima (Lima e Hipólito Yrigoyen)
Horas: 15 en 5 clases de tres horas

ARANCEL DEL CURSO: $3.990

Redes Neuronales Recurrentes - Modelos secuenciales

UNIDAD 1: Redes Neuronales Recurrentes
UNIDAD 2: Natural Language Processing & Word Embeddings
UNIDAD 3: Modelos Secuenciales y mecanismos de atención

Recurrent Neural Networks

Learn about recurrent neural networks. This type of model has been proven to perform extremely well on temporal data. It has several variants including LSTMs, GRUs and Bidirectional RNNs, which you are going to learn about in this section.

Recurrent Neural Networks

Why sequence models
Notation
Recurrent Neural Network Model
Backpropagation through time
Different types of RNNs
Language model and sequence generation
Sampling novel sequences
Vanishing gradients with RNNs
Gated Recurrent Unit (GRU)
Long Short Term Memory (LSTM)
Bidirectional RNN
Deep RNNs


Natural language processing with deep learning is an important combination. Using word vector representations and embedding layers you can train recurrent neural networks with outstanding performances in a wide variety of industries. Examples of applications are sentiment analysis, named entity recognition and machine translation.

Introduction to Word Embeddings

Word Representation
Using word embeddings
Properties of word embeddings
Embedding matrix
Word Embeddings: Word2vec & GloVe
Learning word embeddings
Word2Vec
Negative Sampling
GloVe word vectors


Applications using Word Embeddings

Sentiment Classification
Debiasing word embeddings


Sequence models can be augmented using an attention mechanism. This algorithm will help your model understand where it should focus its attention given a sequence of inputs. This week, you will also learn about speech recognition and how to deal with audio data.

Various sequence to sequence architectures

Basic Models
Picking the most likely sentence
Beam Search
Refinements to Beam Search
Error analysis in beam search
Bleu Score (optional)
Attention Model Intuition
Attention Model
Audio data
Speech recognition
Trigger Word Detection

Conclusion

Conclusion and thank you

El curso de Coursera de Machine Learning de Andrew Ng, profesor de Stanford y uno de los fundadores de Coursera, es posiblemente el paradigma de MOOC. Este curso es uno de los primeros MOOC disponibles y ya lo han cursado cientos de miles de alumnos en todo el mundo. Fue lanzado hace 10 años, aún así es un excelente punto de partida para iniciarse en Machine Learning y disfrutar de las extraordinarias clases del profesor Ng.

En 2017 el profesor Ng lanzó también en Coursera una especialización compuesta de 5 cursos centrados en Deep Learning que despertó gran interés.

Es un curso online, nos juntamos a ver los videos, hay un coordinador que apoya y guía, hay evaluaciones al final de cada semana y se hacen ejercicios prácticos. Se pasan los videos y los ejercicios para seguir estudiando y practicando en la semana.

El curso es en inglés.

La especialización son 5 cursos.

* Neural Networks and Deep Learning
* Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
* Structuring Machine Learning Projects
* Convolutional Neural Networks
* Sequence Models

Este es el quinto curso de la especialización, es un curso muy avanzado

Sobre este Programa Especializado

If you want to break into AI, this Specialization will help you do so. Deep Learning is one of the most highly sought after skills in tech. We will help you become good at Deep Learning.

In five courses, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. You will learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more. You will work on case studies from healthcare, autonomous driving, sign language reading, music generation, and natural language processing. You will master not only the theory, but also see how it is applied in industry. You will practice all these ideas in Python and in TensorFlow, which we will teach.

You will also hear from many top leaders in Deep Learning, who will share with you their personal stories and give you career advice.

AI is transforming multiple industries. After finishing this specialization, you will likely find creative ways to apply it to your work.

We will help you master Deep Learning, understand how to apply it, and build a career in AI.

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