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Preface
PartⅠ. The Fundamentals of Machine Learning
Chapter 1 The Machine Learning Landscape
Chapter 2 End-to-End Machine Learning Project
Chapter 3 Classification
Chapter 4 Training Models
Chapter 5 Support Vector Machines
Chapter 6 Decision Trees
Chapter 7 Ensemble Learning and Random Forests
Chapter 8 Dimensionality Reduction
Part II. Neural Networks and Deep Learning
Chapter 9 Up and Running with TensorFlow
Chapter 10 Introduction to Artificial Neural Networks
Chapter 11 Training Deep Neural Nets
Chapter 12 Distributing TensorFlow Across Devices and Servers
Chapter 13 Convolutional Neural Networks
Chapter 14 Recurrent Neural Networks
Chapter 15 Autoencoders
Chapter 16 Reinforcement Learning
Appendix
A. Exercise Solutions
B. Machine Learning Project Checklist
C. SVM Dual Problem
D. Autodiff
E. Other Popular ANN Architectures
INDEX
PartⅠ. The Fundamentals of Machine Learning
Chapter 1 The Machine Learning Landscape
Chapter 2 End-to-End Machine Learning Project
Chapter 3 Classification
Chapter 4 Training Models
Chapter 5 Support Vector Machines
Chapter 6 Decision Trees
Chapter 7 Ensemble Learning and Random Forests
Chapter 8 Dimensionality Reduction
Part II. Neural Networks and Deep Learning
Chapter 9 Up and Running with TensorFlow
Chapter 10 Introduction to Artificial Neural Networks
Chapter 11 Training Deep Neural Nets
Chapter 12 Distributing TensorFlow Across Devices and Servers
Chapter 13 Convolutional Neural Networks
Chapter 14 Recurrent Neural Networks
Chapter 15 Autoencoders
Chapter 16 Reinforcement Learning
Appendix
A. Exercise Solutions
B. Machine Learning Project Checklist
C. SVM Dual Problem
D. Autodiff
E. Other Popular ANN Architectures
INDEX
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