Programming Machine Learning by Perrotta, Paolo -- Read -- Imperial Library of Trantor

Index

 Acknowledgments  How the Heck Is That Possible?

About This Book Before We Begin

Part I. From Zero to Image Recognition

1. How Machine Learning Works

Programming vs. Machine Learning Supervised Learning The Math Behind the Magic Setting Up Your System

2. Your First Learning Program

Getting to Know the Problem Coding Linear Regression Adding a Bias What You Just Learned Hands On: Tweaking the Learning Rate

3. Walking the Gradient

Our Algorithm Doesn’t Cut It Gradient Descent What You Just Learned Hands On: Basecamp Overshooting

4. Hyperspace!

Adding More Dimensions Matrix Math Upgrading the Learner Bye Bye, Bias A Final Test Drive What You Just Learned Hands On: Field Statistician

5. A Discerning Machine

Where Linear Regression Fails Invasion of the Sigmoids Classification in Action What You Just Learned Hands On: Weighty Decisions

6. Getting Real

Data Come First Our Own MNIST Library The Real Thing What You Just Learned Hands On: Tricky Digits

7. The Final Challenge

Going Multiclass Moment of Truth What You Just Learned Hands On: Minesweeper

8. The Perceptron

Enter the Perceptron Assembling Perceptrons Where Perceptrons Fail A Tale of Perceptrons

Part II. Neural Networks

9. Designing the Network

Assembling a Neural Network from Perceptrons Enter the Softmax Here’s the Plan What You Just Learned Hands On: Network Adventures

10. Building the Network

Coding Forward Propagation Cross Entropy What You Just Learned Hands On: Time Travel Testing

11. Training the Network

The Case for Backpropagation From the Chain Rule to Backpropagation Applying Backpropagation Initializing the Weights The Finished Network What You Just Learned Hands On: Starting Off Wrong

12. How Classifiers Work

Tracing a Boundary Bending the Boundary What You Just Learned Hands On: Data from Hell

13. Batchin’ UpBatchin’ Up

Learning, Visualized Batch by Batch Understanding Batches What You Just Learned Hands On: The Smallest Batch

14. The Zen of Testing

The Threat of Overfitting A Testing Conundrum What You Just Learned Hands On: Thinking About Testing

15. Let’s Do DevelopmentLet’s Do Development

Preparing Data Tuning Hyperparameters The Final Test Hands On: Achieving 99% What You Just Learned… and the Road Ahead

Part III. Deep Learning

16. A Deeper Kind of Network

The Echidna Dataset Building a Neural Network with Keras Making It Deep What You Just Learned Hands On: Keras Playground

17. Defeating Overfitting

Overfitting Explained Regularizing the Model A Regularization Toolbox What You Just Learned Hands On: Keeping It Simple

18. Taming Deep Networks

Understanding Activation Functions Beyond the Sigmoid Adding More Tricks to Your Bag What You Just Learned Hands On: The 10 Epochs Challenge

19. Beyond Vanilla Networks

The CIFAR-10 Dataset The Building Blocks of CNNs Running on Convolutions What You Just Learned Hands On: Hyperparameters Galore

20. Into the Deep

The Rise of Deep Learning Unreasonable Effectiveness Where Now? Your Journey Begins

A1. Just Enough Python

What Python Looks Like Python’s Building Blocks Defining and Calling Functions Working with Modules and Packages Creating and Using Objects That’s It, Folks!

A2. The Words of Machine Learning

← Prev
Back
Next →

← Prev
Back
Next →