Table of Contents

1. Preface
   1. Who this book is for
   2. What this book covers
   3. To get the most out of this book
      1. Download the example code files
      2. Conventions used
   4. Get in touch
      1. Reviews
1. Machine Learning Toolkit
   1. Installing Docker
   2. The machine learning Docker file
   3. Sharing data
   4. Machine learning REST service
   5. Summary
2. Image Data
   1. MNIST digits
   2. Tensors – multidimensional arrays
   3. Turning images into tensors
   4. Turning categories into tensors
   5. Summary
3. Classical Neural Network
   1. Comparison between classical dense neural networks
   2. Activation and nonlinearity
   3. Softmax
   4. Training and testing data
   5. Dropout and Flatten
   6. Solvers
   7. Hyperparameters
   8. Grid searches
   9. Summary
4. A Convolutional Neural Network
   1. Convolutions
   2. Pooling
   3. Building a convolutional neural network
   4. Deep neural network
   5. Summary
5. An Image Classification Server
   1. REST API definition
   2. Trained models in Docker containers
   3. Making predictions
   4. Summary
6. Other Books You May Enjoy
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