Grokking deep learning

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Grokking Deep Learning MEAP V03......Page 1 Copyright......Page 2 Welcome......Page 3 Brief contents......Page 4 Chapter 1: Introducing Deep Learning: Why you should learn it......Page 5 Why should you learn Deep Learning?......Page 6 Why you should read this book!......Page 7 What you need to get started......Page 9 Conclusion and Primer for Chapter 2......Page 10 Chapter 2: Fundamental Concepts: How do machines learn?......Page 11 What is Deep Learning?......Page 12 What is Machine Learning?......Page 13 Supervised Machine Learning......Page 14 Unsupervised Machine Learning......Page 15 Parametric vs Non-Parametric Learning......Page 16 Supervised Parametric Learning......Page 17 Step 3: Learn the Pattern......Page 18 Unsupervised Parametric Learning......Page 19 Conclusion......Page 20 Chapter 3: Building Your First Neural Network: Introduction to Gradient Descent......Page 21 What am I going to learn in this chapter?......Page 22 What is a Neural Network?......Page 24 What does a Neural Network do?......Page 25 Does the network make accurate predictions?......Page 26 Why measure error?......Page 27 What the Simplest Form of Neural Network Learning?......Page 28 Characteristics of Hot and Cold Learning......Page 29 Calculating Both direction and amount from error......Page 30 Learning Is Just Reducing Error......Page 32 Let's Back Up And Talk about Functions......Page 34 Relationship Exploration: Hot and Cold......Page 36 A Box With Rods Poking Out of It......Page 37 Derivatives... take Two......Page 38 What you don't really need to know.........Page 39 How to use a derivative to learn......Page 40 Where is our derivative in the code?......Page 41 Learning Method: Gradient Descent......Page 42 Breaking Gradient Descent......Page 43 Divergence......Page 44 Introducing.... Alpha......Page 45 Alpha In Code......Page 46 Memorizing......Page 47 Chapter 4: Neural Networks with Multiple Inputs: Leveraging Matrix Algebra......Page 48 Expanding to Multiple Input Values......Page 49 What We're Going to Build......Page 50 The Street Light Problem......Page 51 Preparing our Data......Page 53 Matrices and the Matrix Relationship......Page 54 Creating a Matrix or Two in Python......Page 57 What is a Neural Network......Page 58 How Does A Neural Network Predict?......Page 59 How Does A Neural Network Learn?......Page 60 Slow down! What is "delta"?......Page 61 Building our Neural Network in Python......Page 62 Teaching Our Neural Network in Python......Page 63 Quick Review from 10,000 Feet......Page 64 Putting it all together in Python (verbose version)......Page 65 Putting it all together in Python (polished version)......Page 66 The Differences Line by Line......Page 67 Learning the whole dataset!......Page 68 Write it from Memory!......Page 69 Chapter 5: Building Your First "Deep" Neural Network: Introduction to Backpropagation......Page 70 Neural Networks Learn Correlation......Page 71 Up and Down Pressure......Page 72 Up and Down Pressure (cont.)......Page 73 Edge Case: Overfitting......Page 74 Edge Case: Conflicting Pressure......Page 75 Edge Case: Conflicting Pressure (cont.)......Page 76 Learning Indirect Correlation......Page 77 Creating Our Own Correlation......Page 78 Stacking Neural Networks?......Page 79 Backpropagation: Long Distance Error Attribution......Page 80 Backpropagation: Why does this work?......Page 81 Why The Neural Network Still Doesn't Work......Page 82 The Secret to "Sometimes Correlation"......Page 84 A Quick Break......Page 85 Our New Prediction Code......Page 86 What We Will Build......Page 87 What It Looks Like......Page 88 A Non-Linear Problem......Page 89 A Non-Linear Solution......Page 90