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Programming Machine Learning: From Coding to Deep Learning


Cover image for Programming Machine Learning

Programming Machine Learning

From Coding to Deep Learning


You’ve decided to tackle machine learning — because you’re job hunting, embarking on a new project, or just think self-driving cars are cool. But where to start? It’s easy to be intimidated, even as a software developer. The good news is that it doesn’t have to be that hard. Master machine learning by writing code one line at a time, from simple learning programs all the way to a true deep learning system. Tackle the hard topics by breaking them down so they’re easier to understand, and build your confidence by getting your hands dirty.

Printed in full color.

Customer Reviews

As a developer with more than 20 years of experience but with no background in
machine learning, I found this book to be pure gold. It explains the math behind
machine learning in a very intuitive way that is easy to understand.

- Giancarlo Valente

Agile Coach, auLAB Co-Founder

Let me say that I think this is a brilliant book. It takes the reader step by step
through the thinking behind machine learning. Combine that with Paolo’s fun
approach and this is the book I’d suggest every machine learning neophyte
start with.

- Russ Olsen

Author, "Getting Clojure" and "Eloquent Ruby"

This book is totally engaging. I love the humor, and the way Paolo talks as a
buddy who understands your fears and guides you through as someone who has
gone through the same learning process.

- Alberto Lumbreras

Research Scientist, Criteo AI Lab

Programming Machine Learning is a well-organized and accessible introduction to
machine learning for programmers. The book eschews traditional mathematically
centric explanations for programming centric ones, and as a result, it makes
foundational concepts readily accessible.

- Dan Sheikh

Lead Engineer, BCG Digital Ventures

See All Reviews

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What You Need

The examples in this book are written in Python, but don’t worry if you don’t know this language: you’ll pick up all the Python you need very quickly. Apart from that, you’ll only need your computer, and your code-adept brain.

Contents & Extracts

  • How the Heck Is That Possible?
  • From Zero to Image Recognition
    • How Machine Learning Works
    • 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
    • Walking the Gradient
      • Our Algorithm Doesn’t Cut It
      • Gradient Descent
      • What You Just Learned
      • Hands On: Basecamp Overshooting
    • Hyperspace!
      • Adding More Dimensions
      • Matrix Math
      • Upgrading the Learner
      • Bye Bye, Bias
      • A Final Test Drive
      • What You Just Learned
      • Hands On: Field Statistician
    • A Discerning Machine
      • Where Linear Regression Fails
      • Invasion of the Sigmoids
      • Classification in Action
      • What You Just Learned
      • Hands On: Weighty Decisions
    • Getting Real excerpt
      • Data Come First
      • Our Own MNIST Library
      • The Real Thing
      • What You Just Learned
      • Hands On: Tricky Digits
    • The Final Challenge
      • Going Multiclass
      • Moment of Truth
      • What You Just Learned
      • Hands On: Minesweeper
    • The Perceptron
      • Enter the Perceptron
      • Assembling Perceptrons
      • Where Perceptrons Fail
      • A Tale of Perceptrons
  • Neural Networks
    • Designing the Network
      • Assembling a Neural Network from Perceptrons
      • Enter the Softmax
      • Here’s the Plan
      • What You Just Learned
      • Hands On: Network Adventures
    • Building the Network
      • Coding Forward Propagation
      • Cross Entropy
      • What You Just Learned
      • Hands On: Time Travel Testing
    • 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
    • How Classifiers Work
      • Tracing a Boundary
      • Bending the Boundary
      • What You Just Learned
      • Hands On: Data from Hell
    • Batchin’ Up
      • Learning, Visualized
      • Batch by Batch
      • Understanding Batches
      • What You Just Learned
      • Hands On: The Smallest Batch
    • The Zen of Testing
      • The Threat of Overfitting
      • A Testing Conundrum
      • What You Just Learned
      • Hands On: Thinking About Testing
    • Let’s Do Development
      • Preparing Data
      • Tuning Hyperparameters
      • The Final Test
      • Hands On: Achieving 99%
      • What You Just Learned… and the Road Ahead
  • Deep Learning
    • 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
    • Defeating Overfitting
      • Overfitting Explained
      • Regularizing the Model
      • A Regularization Toolbox
      • What You Just Learned
      • Hands On: Keeping It Simple
    • 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
    • Beyond Vanilla Networks
      • The CIFAR-10 Dataset
      • The Building Blocks of CNNs
      • Running on Convolutions
      • What You Just Learned
      • Hands On: Hyperparameters Galore
    • Into the Deep
      • The Rise of Deep Learning
      • Unreasonable Effectiveness
      • Where Now?
      • Your Journey Begins


Paolo Perrotta is a traveling software mentor. He wrote “Metaprogramming Ruby” for the Pragmatic Programmers, and produced the popular “How Git Works” training for Pluralsight. He speaks a lot — at conferences and, according to his friends and family, pretty much anywhere else.