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Table of Contents
Prefacexi
Chapter 1: Neural Network Foundations with TensorFlow 2.0 1
What is TensorFlow (TF)? 1
What is Keras? 3
What are the most important changes in TensorFlow 2.0? 3
Introduction to neural networks 5
Perceptron6
A first example of TensorFlow 2.0 code 7
Multi-layer perceptron – our first example of a network 8
Problems in training the perceptron and their solutions 9
Activation function – sigmoid 10
Activation function – tanh 10
Activation function – ReLU 11
Two additional activation functions – ELU and LeakyReLU 12
Activation functions 13
In short – what are neural networks after all? 13
A real example – recognizing handwritten digits 14
One-hot encoding (OHE) 14
Defining a simple neural network in TensorFlow 2.0 15
Running a simple TensorFlow 2.0 net and establishing a baseline 20
Improving the simple net in TensorFlow 2.0 with hidden layers 21
Further improving the simple net in TensorFlow with Dropout 24
Testing different optimizers in TensorFlow 2.0 26
Increasing the number of epochs 32
Controlling the optimizer learning rate 33
Increasing the number of internal hidden neurons 34
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