The Fundamentals of Machine Learning

Chapter 1 The Machine Learning Landscape

1. What Is Machine Learning?
2. Why Use Machine Learning?
3. Types of Machine Learning Systems
4. Main Challenges of Machine Learning
5. Testing and Validating
6. Exercises

Chapter 2 End-to-End Machine Learning Project

1. Working with Real Data
2. Look at the Big Picture
3. Get the Data
4. Discover and Visualize the Data to Gain Insights
5. Prepare the Data for Machine Learning Algorithms
6. Select and Train a Model
7. Fine-Tune Your Model
8. Launch, Monitor, and Maintain Your System
9. Try It Out!
10. Exercises

Chapter 3 Classification

1. MNIST
2. Training a Binary Classifier
3. Performance Measures
4. Multiclass Classification
5. Error Analysis
6. Multilabel Classification
7. Multioutput Classification
8. Exercises

Chapter 4 Training Models

1. Linear Regression
2. Gradient Descent
3. Polynomial Regression
4. Learning Curves
5. Regularized Linear Models
6. Logistic Regression
7. Exercises

Chapter 5 Support Vector Machines

1. Linear SVM Classification
2. Nonlinear SVM Classification
3. SVM Regression
4. Under the Hood
5. Exercises

Chapter 6 Decision Trees

1. Training and Visualizing a Decision Tree
2. Making Predictions
3. Estimating Class Probabilities
4. The CART Training Algorithm
5. Computational Complexity
6. Gini Impurity or Entropy?
7. Regularization Hyperparameters
8. Regression
9. Instability
10. Exercises

Chapter 7 Ensemble Learning and Random Forests

1. Voting Classifiers
2. Bagging and Pasting
3. Random Patches and Random Subspaces
4. Random Forests
5. Boosting
6. Stacking
7. Exercises

Chapter 8 Dimensionality Reduction

1. The Curse of Dimensionality
2. Main Approaches for Dimensionality Reduction
3. PCA
4. Kernel PCA
5. LLE
6. Other Dimensionality Reduction Techniques
7. Exercises

Neural Networks and Deep Learning

Chapter 9 Up and Running with TensorFlow

1. Installation
2. Creating Your First Graph and Running It in a Session
3. Managing Graphs
4. Lifecycle of a Node Value
5. Linear Regression with TensorFlow
6. Implementing Gradient Descent
7. Feeding Data to the Training Algorithm
8. Saving and Restoring Models
9. Visualizing the Graph and Training Curves Using TensorBoard
10. Name Scopes
11. Modularity
12. Sharing Variables
13. Exercises

Chapter 10 Introduction to Artificial Neural Networks

1. From Biological to Artificial Neurons
2. Training an MLP with TensorFlow’s High-Level API
3. Training a DNN Using Plain TensorFlow
4. Fine-Tuning Neural Network Hyperparameters
5. Exercises

Chapter 11 Training Deep Neural Nets

1. Vanishing/Exploding Gradients Problems
2. Reusing Pretrained Layers
3. Faster Optimizers
4. Avoiding Overfitting Through Regularization
5. Practical Guidelines
6. Exercises

Chapter 12 Distributing TensorFlow Across Devices and Servers

1. Multiple Devices on a Single Machine
2. Multiple Devices Across Multiple Servers
3. Parallelizing Neural Networks on a TensorFlow Cluster
4. Exercises

Chapter 13 Convolutional Neural Networks

1. The Architecture of the Visual Cortex
2. Convolutional Layer
3. Pooling Layer
4. CNN Architectures
5. Exercises

Chapter 14 Recurrent Neural Networks

1. Recurrent Neurons
2. Basic RNNs in TensorFlow
3. Training RNNs
4. Deep RNNs
5. LSTM Cell
6. GRU Cell
7. Natural Language Processing
8. Exercises

Chapter 15 Autoencoders

1. Efficient Data Representations
2. Performing PCA with an Undercomplete Linear Autoencoder
3. Stacked Autoencoders
4. Unsupervised Pretraining Using Stacked Autoencoders
5. Denoising Autoencoders
6. Sparse Autoencoders
7. Variational Autoencoders
8. Other Autoencoders
9. Exercises

Chapter 16 Reinforcement Learning

1. Learning to Optimize Rewards
2. Policy Search
3. Introduction to OpenAI Gym
4. Neural Network Policies
5. Evaluating Actions: The Credit Assignment Problem
6. Policy Gradients
7. Markov Decision Processes
8. Temporal Difference Learning and Q-Learning
9. Learning to Play Ms. Pac-Man Using Deep Q-Learning
10. Exercises
11. Thank You!

Appendix Exercise Solutions

1. Chapter 1: The Machine Learning Landscape
2. Chapter 2: End-to-End Machine Learning Project
3. Chapter 3: Classification
4. Chapter 4: Training Linear Models
5. Chapter 5: Support Vector Machines
6. Chapter 6: Decision Trees
7. Chapter 7: Ensemble Learning and Random Forests
8. Chapter 8: Dimensionality Reduction
9. Chapter 9: Up and Running with TensorFlow
10. Chapter 10: Introduction to Artificial Neural Networks
11. Chapter 11: Training Deep Neural Nets
12. Chapter 12: Distributing TensorFlow Across Devices and Servers
13. Chapter 13: Convolutional Neural Networks
14. Chapter 14: Recurrent Neural Networks
15. Chapter 15: Autoencoders
16. Chapter 16: Reinforcement Learning

Appendix Machine Learning Project Checklist

1. Frame the Problem and Look at the Big Picture
2. Get the Data
3. Explore the Data
4. Prepare the Data
5. Short-List Promising Models
6. Fine-Tune the System
7. Present Your Solution
8. Launch!