GOAL

To develop a model that answers a question

2025-08-18_v1.0

Algorithms & Machine Learning

Applied Machine Learning with Catapults

Breakout Development Team

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JAMES VENDITTO

College: University of Notre Dame

Major: Electrical Engineering

JOANNA CAUDLE

College: Georgia Institute of Technology

Major: Mechanical Engineering

Industry Experience: Bechtel Power Corporation

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ANSWER ME!

Machine learning is like the human brain because…

Type your answer here

Watch Me

Experiment with this simple machine learning by clicking here.

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• In the left hand box, draw a digit from 0 to 9 and see if the program can recognize it.

Experiment and Reflect

Reflect: Can you think of 3 examples of machine learning in your everyday life?

ANSWER ME

The Machine Learning algorithm creates a mathematical model that can answer questions like “Is that a dog or a cat?”, and “What number is that?”.

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The mathematical model is created using data and becomes more accurate as it collects more data.

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How Does the Computer Do That?

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The Essential Question

How can I predict the settings on a catapult needed to hit a specific target?

In the rest of this lesson, you will create a mathematical model of a simple machine (the catapult).

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You will do this by building a catapult model and collecting data in order to answer the question:

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Catapults are Found in:

Although they are all the same thing with different designs,

they were all made with the same process:

ANCIENT WEAPONS

GAMES

TOYS

THE ENGINEERING DESIGN PROCESS

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Catapult Terms

LEVER

A simple machine used to easily lift or move objects

FULCRUM

The support that a lever rests on and pivots about

PROJECTILE (WEIGHT/PAYLOAD)

The object that a catapult launches

ARM (LEVER)

The lever that a catapult uses to launch a projectile

STRUCTURE

Includes the BASE, FRAME, and STOP

POTENTIAL ENERGY

Energy that is stored to be used later

KINETIC ENERGY

Energy of a moving object

BASE

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Think

Imagine you are a mechanical engineer building a catapult. How would you use the Engineering Design Process to create a catapult that can accurately hit a given distance?

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What action would you take with each step of the process?

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Step One:

Define the Problem

ANSWER ME! What problem or challenge are you trying to solve?

Type your answer here

Type your answer here

Step Two: Research

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WHAT MATERIALS WILL YOU NEED? WHAT DESIGN COULD YOU USE?

IDEAS:

Design Constraints

• Using materials provided in your kit, create a design for a catapult
• You can also use materials found around your house such as plastic spoons, rubber bands, and even pencils

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Testing Restrictions

• Your catapult should be freestanding (the catapult should be able to support itself without you holding it up)
• You can support your catapult while testing - this means that if you need to put your hand on the base while pulling the lever back, that’s okay

Design & Testing Restrictions

SOME DESIGN IDEAS:

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Lab Materials

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Step Three:

Design Solution

ANSWER ME! Draw a diagram of your design. Insert photos here.

Insert images here.

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Step Four:

Build

ANSWER ME! Create your first prototype of your catapult. Insert photos here.

Insert images here.

HINT: If you are struggling with

your catapult design, check out

this video.

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Step Five: Test

You are almost ready to test your catapult!

But first, we need to go through a few concepts:

• Factors and Responses
• Example Data Charts

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The next 4 slides will review these ideas.

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Factor vs. Response

FACTOR: “Cause”

RESPONSE: “Effect”

• An outcome
• Dependent variable
• Examples:
• Distance ball travelled
• Max height of ball

• Something you control
• Independent variable
• Examples:
• Height of catapult
• Catapult angle
• # of rubber bands

Answer the Following

INSTRUCTIONS: Drag the items on the right to the correct category. Use the slide prior to help you.

CAUSE

EFFECT

INDEPENDENT VARIABLE

DEPENDENT VARIABLE

WHAT YOU CONTROL

THE OUTCOME

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ANSWER ME!

What do you notice about the data? What do you wonder about it?

Type your answer here

Analyze Data in the Table

Look at the Example Data:

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Look at the

Example Data:

Notice the height does not change.

If you do not have the ability to measure the exact angle, you can use approximations.

For example, “½ way pulled” or “⅓ way pulled”

It is important to only change ONE variable at a time.

This way, you can identify which change to your catapult causes which outcome.

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Step Five: Test

Fill out the chart in order to collect and organize your data.

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Step Six: Analyze Data

USE EXCEL IN ORDER TO PUT YOUR DATA INTO A PLOT AND GRAPH.

INSERT;

SCATTER PLOT

HIGHLIGHT COLUMNS

YOUR GRAPH SHOULD LOOK LIKE THIS, ADD CHART ELEMENTS FOR BETTER PRESENTATION

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ADD A TRENDLINE ON YOUR MODEL

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• Click “Add chart element”, trendline, then linear)
• Add all of the chart elements you want to make your graph look presentable, including titles, colors, etc.

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Step Six: Analyze Data

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Step Six: Analyze Data

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Take a screenshot of your final graph and insert it here.

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Your Catapult is Machine Learning

CATAPULT INPUT

Pull back angle, height, number of rubber bands, etc.

CATAPULT OUTPUT

Distance ball predicted to travel shown by trendline on Excel graph

Student Analyzes:

Step Six: Analyze Data

Answer the following questions with the data you collected.

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Reflect on Your Design and Results

ANSWER ME!

Complete the mandatory 5-minute Exit Ticket by clicking here!

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Optional Extensions:

• Create a Virtual AI Sorter with Google’s Teachable Machine
• Play with a Neural Network Simulator in TensorFlow

This section will provide an overview of the extension lab(s) and/or optional activity(s).

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• These activities are opportunities for students to dive deeper and ideate.
• The materials associated with the extension labs may not provide as many detailed instructions as the main lab activity.

Create a Virtual AI Sorter with Google’s Teachable Machine

What you’ll be doing:

• Designing a AI sorter using pictures of various materials that you find

Ideal option if you:

• Want to use the engineering design process to test technology.

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Play with a Neural Network Simulator in TensorFlow

What you’ll be doing:

• Tinker with a simple neural network in TensorFlow Playground

Ideal option if you:

• Want a better understanding of how a neural network is constructed.

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Optional Extension Activities

Any text here?

Teachable Machine Sorter

Neural Networks

Teachable Machine Sorter

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The Essential Question

How can we create a machine that sorts through different recyclable material effectively?

In this extension activity, you will create a sorter using machine learning.

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You will do this by building an AI sorter model and collecting data in order to answer the question:

• Agriculture and Farming.
• Autonomous Flying
• Retail, Shopping and Fashion
• Security and Surveillance
• Sports Analytics and Activities
• Manufacturing and Production
• Live Stock and Inventory Management

Artificial Intelligence (AI) is used in:

Although they are all the same thing with different designs, they were all made with

the same process:

THE ENGINEERING DESIGN PROCESS

• In 2018, the total generation of municipal solid waste (MSW) was 292.4 million tons.
• Approximately 69 million tons were recycled and 25 million tons were composted, which is equivalent to a 32.1% recycling and composting rate.
• Nearly 35 million tons of MSW were combusted and more than 146 million tons of MSW were landfill.

Research on the Problem

HOWEVER, if there was better technology available to sort through this waste, the recycling and composting rate could be increased!

• AI-Powered Recycling Robots could help solve the plastic crisis!
• Engineering researchers are developing a unique method to increase the recycling of soft plastics by creating a smart robot that can identify, sort, and separate different types of recyclable waste.
• However, they are currently able to only identify a limited amount of materials, are expensive, and not able to process the significant amount of waste recycling plants receive.

Current Solutions

• Glass
• Wood
• Metal
• Paper
• Plastic
• Rubber
• Cardboard

Design Considerations: Materials

What kind of materials/products can be identified to make recycling more efficient?

• E-Waste (electronics, batteries, etc.)
• Compost (Food, garden trimmings, etc.)
• Hazardous waste (paint cans, pesticides, etc.)
• Construction waste (concrete, roof shingles, etc.)

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Think

Imagine you are a team of computer engineers building an AI sorter. How would you use the Engineering Design Process to create a sorter that can accurately and reliably identify a material?

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What action would you take with each step of the process?

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Step Six: Analyze Data

1. Define the Problem: What problem or challenge are you trying to solve?
2. Research: What materials will you need?
3. Design Solution: Draw out a diagram of your machine.
4. Build Prototype: Provide samples to your teachable machine.
5. Test: Show your machine the different materials you taught it and record the material it identifies.
6. Analyze Your Results: Use your observations and data from step 5 to answer the questions in your student workbook.

How to use Teachable Machine:

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LINK: https://teachablemachine.withgoogle.com

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GATHER: Gather and group your example into classes, or categories, that you want the computer to learn.

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TRAIN: Train your model, then instantly test it out to see whether it can correctly classify new examples.

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Testing: Using Teachable Machine

Create an AI sorter that sorts between one of the following options:

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• Plastic vs Metal
• Paper vs Compost
• Cardboard vs Plastic

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NOTE: Think about the variety of materials you have in your class to use as samples (ex. Plastic→bottle, container, grocery bag)

Testing: Option 1

Create an AI sorter that sorts between at least two materials.

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Be creative, and think about what would make recycling more efficient now!

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If you need help thinking of materials, look back at slides 33 & 37.

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You can also conduct research on what materials are commonly found at landfills/recycling plants and create an AI sorter that identifies materials that concern you most.

Testing: Option 2

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Testing Efficiency

What is a sample?

A sample is how many examples the machine has been given of a specific category during the “train” portion (material in this lab).

You will then compare the efficiencies of these trials.

Users can hold down the train button instead of clicking it to get a stream of photos (similar to a video).

Whether you choose to do Option 1 or 2, try two separate trials where Trial 1 has less samples than Trial 2.

TRIAL 1:

TRIAL 2:

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Define the Problem

ANSWER ME! What problem or challenge are you trying to solve?

Type your answer here

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Research

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Write your answer here.

ANSWER ME! What are the most common recycled materials. What categories do facilities sort materials into?

How is most recycling sorting done?

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Build

Insert screenshots of your samples here.

ANSWER ME! Find samples to teach your machine. Create your categories to store images in. Feed your images into the AI and build the model.

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Trial 1

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Trial 2

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Analyze Data

ANSWER THE FOLLOWING QUESTIONS WITH THE DATA YOU COLLECTED.

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According to your results on the previous slides, what was the efficiency of your machine?

Efficiency = # of correct identifications *100

# of trials

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Reflection

Add your responses here.

ANSWER ME! Take a moment to reflect on what you have learned from this project with a partner: what materials did you use, how many samples were collected in each trial, how did your efficiencies in different trials compare to each other, and what percentage of the time did it guess correctly? Is there a correlation between the number of samples taken and the accuracy? Take note of what went well and learn from what went wrong.

Create a Virtual AI Sorter with Google’s Teachable Machine

What you’ll be doing:

• Designing a AI sorter using pictures of various materials that you find

Ideal option if you:

• Want to use the engineering design process to test technology.

Play with a Neural Network Simulator in TensorFlow

What you’ll be doing:

• Tinker with a simple neural network in TensorFlow Playground

Ideal option if you:

• Want a better understanding of how a neural network is constructed.

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Optional Extension Activities

Any text here?

Teachable Machine Sorter

Neural Networks

Build a Neural Network

Extension

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Extension Development Team

MARY BRUSCO

College: University of Notre Dame ‘26

Major: Computer Science & Engineering

MARY JARRATT

College: Virginia Tech, Ph.D. student ‘24

Major: Engineering & Robotics Education

JOANNA CAUDLE

College: Georgia Institute of Technology

Major: Mechanical Engineering

Industry Experience: Bechtel Power Corporation

JASUN BURDICK

College: University of Central Florida

Major: Industrial Engineering

Industry Experience: Building Automation, Robotics

In the last activity, you used the Engineering Design Process (EDP) to build a catapult and model its behavior. You learned about the problem using data, developed a model for your catapult, and iteratively tested your design to make your catapult more accurate and precise, all without step-by-step instructions!

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We mentioned that machine learning is a way for computers to do the same: learn from data, build a model for solving a problem, and then iteratively test that model to make it more accurate and precise.

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There’s just one problem: How do we program a computer to build a model on its own?

Introduction

There’s something about your brain and the way it’s structured that allows it to analyze data, come up with solutions to problems, and learn from its mistakes to build even better solutions. Your brain is an example of a powerful computer that can use EDP to solve problems on its own!

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So, since your brain can do what we want our computer to do, let’s give our computer a brain.

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Specifically, instead of writing step-by-step programs for each problem we want our computer to solve, we’ll make a program that mimics the structure of your brain to hopefully give our machine the ability to “learn.” This “machine brain” is what we call a neural network.

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NEURAL NETWORK

• A type of computer program modeled after the human brain that allows computers to make decisions about data on their own, without step-by-step instructions.

Solution: Your Brain!

IN THIS EXTENSION…

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You will explore neural networks by building one in a virtual playground called TensorFlow. By the end, you’ll have built a neural network that can classify points in a data set all by itself!

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What You’ll Do In This Extension

1. Introduction to Neural Networks with TensorFlow
2. Connection to the Catapult Activity
3. Tinker with the TensorFlow Playground
4. Learn How the Model Works
1. Data Input
2. Neurons and Weighting
3. Error Reduction
5. The Challenge - Build a Simple Model

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Part I: TensorFlow

TensorFlow is a free and open-source software library for machine learning and artificial intelligence. It can be used across a range of tasks but has a particular focus on training and inference using neural networks.

TensorFlow has a playground that lets you expand your understanding of neural networks by developing a simple model to sort a collection of data points.

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Part I: TensorFlow Playground

• The playground simulation attempts to build a neural network model consisting of Features and layers of Neurons that predicts where the colored dots are
• Features are inputs to the model. Neurons that have particular shapes are what the computer program uses to build a model for classifying the data.
• By weighting and summing connections into neurons you can build more advanced detection

Part II: Connection to our Catapult Activity

Think of the student as the machine/computer

The more input data you give it the more accurate the model is getting.

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Your Catapult is Machine Learning

CATAPULT INPUT

Pull back angle, height, number of rubber bands, etc.

CATAPULT OUTPUT

Distance ball predicted to travel shown by trendline on Excel graph

Think of the features of the neural network the same way you think of the different features of your catapult.

Intuitively, the student learns that an important feature of the catapult is the force you push down on it to make it go farther. Similarly, the computer model learns by guessing and checking which features to emphasize more. The model shows which features are more important to the output by assigning a heavier weight (we see this through the thickness of the line coming from a feature or neuron).

Think of the importance you give each feature in the catapult as the weight of importance the computer program gives each feature in the network.

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Part III: Introduction to the Playground

WATCH ME

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Part III: Tinker in the Playground

• Go to the playground and try running the model a few times by pressing the play and reset buttons
• Watch the model learn to reduce error
• Change between data sets and see how it changes.
• Look at the Training Loss Graph after each data set. With the sample setup, the model is only about 50% correct on some of the data sets, which is just like guessing or flipping a coin.

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Part IV: How does the model work?

The TensorFlow Playground has 4 data sets comprising the shapes on the left.

• Circle - Top left
• Exclusive OR - Top right
• Gaussian - Bottom left
• Spiral - Bottom right

Data is broken up into training and testing (validation)

These data sets are similar to the written number data set you saw in the handwriting example in the Machine Learning introductory video.

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Neural Network: Data features

Some datasets have easier features to define. (simpler shape)

ANSWER ME!

What data set has the simplest shape in your opinion? The most complex?

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Neural Network: How does it work?

How do you write code that classifies whether a data point is orange or blue? Perhaps you could draw an arbitrary diagonal line between the two groups like that shown on the next slide. You could define a threshold to determine in which group each data point belongs.

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Let's look at a simple classification problem. Imagine you have a dataset such as the one below. There are two groups of data (blue and orange) that the model is searching for. Each data point has two values X₁ (horizontal axis) and X₂ (vertical axis).

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Neural Network: How does the program work?

If x₁ + x₂ > some threshold value → Then the dot is blue

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But the thing is, a programmer has to find appropriate values for variables and instruct the computer how to classify the data points. That’s A LOT of steps of code!

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A neural network computer program uses artificial neurons and trains itself to classify the data.

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Neural Network: Neurons and Weights

• Neurons

Neurons are formed by combining the weighted inputs of features.

In a simple neural network model, the mathematical equation might look something like this:

If w₁x₁ + w₂x₂ > some threshold value, ��Then the dot is blue, where w₁ and w₂ are the weights given to each feature

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• Layers

Adding layers allows the program to combine previous neurons to make a more complex model to classify data

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Part V: Challenge: Build Your Own Neural Network

Requirements: Build the simplest model that has the lowest error rate.

Plan: Try selecting data sets, features, layers, and neurons

Run the model for short bursts (< 1,000 epoch)

Note: an epoch is one training cycle for the model

Record the testing loss (error rate)

Improve: Change an input and rerun

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Challenge

As you gain understanding of how the model is working try this challenge:

1. Build a model with 5 items: features + neurons. (below has 4)
2. Test your model against the 4 data sets without altering it
3. Record your testing loss for each at < 1,000 epoch in the table below.
4. Sum your total error

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Link to the: Playground

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Challenge: It's in the details

Repeat now with a larger model and you get more control options.

Use this link now: Neural Network with More Options

1. Build a model with 10 items: features + neurons.
2. Record your testing loss for each at ~1,000 epoch.

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Next Steps: How is machine learning used in business?

Machine learning is being used in the banking, healthcare and music and entertainment industries to name a few.

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Check out this case �study from Netflix �about how machine �learning is used �in their organization.

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Next Steps: Want to learn more?

Here are some concepts you may be interested in and also a great video that further explains neural networks:

• Neural Networks
• Linear Algebra
• Calculus

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Continue to Explore

If you liked today’s breakout, you may be interested in these topics:

• Mechanics - Projectile Motion
• 2D Physics Modelling
• Machine Learning
• Computer Algorithms
• Statistics - Regression

Types of engineering relevant to the Catapult Building:

• Mechanical Engineering
• Civil Engineering
• Mechatronics

Types of engineering relevant to Iteration and Machine Learning:

• Computer Science
• Computer Engineering
• Systems Engineering

Thank you!

Any text here?

Follow up info here?