Chapter 1, The Building Blocks of Deep Learning, reviews some basics around the operation of neural networks, touches on optimization algorithms, talks about model validation, and goes over setting up a development environment suitable for building deep neural networks.
Chapter 2, Using Deep Learning to Solve Regression Problems, enables you build very simple neural networks to solve regression problems and explore the impact of deeper more complex models on those problems.
Chapter 3, Monitoring Network Training Using TensorBoard, lets you get started right away with TensorBoard, which is a wonderful application for monitoring and debugging your future models.
Chapter 4, Using Deep Learning to Solve Binary Classification Problems, helps you solve binary classification problems using deep learning.
Chapter 5, Using Keras to Solve Multiclass Classification Problems, takes you to multiclass classification and explores the differences. It also talks about managing overfitting and the safest choices for doing so.
Chapter 6, Hyperparameter Optimization, shows two separate methods for model tuning—one, well-known and battle tested, while the other is a state-of-the-art method.
Chapter 7, Training a CNN From Scratch, teaches you how to use convolutional networks to do classification with images.
Chapter 8, Transfer Learning with Pretrained CNNs, describes how to apply transfer learning to get amazing performance from an image classifier, even with very little data.
Chapter 9, Training an RNN from scratch, discusses RNNs and LSTMS, and how to use them for time series forecasting problems.
Chapter 10, Training LSTMs with Word Embeddings From Scratch, continues our conversation on LSTMs, this time talking about natural language classification tasks.
Chapter 11, Training Seq2Seq Models, helps us use sequence to sequence models to do machine translation.
Chapter 12, Using Deep Reinforcement Learning, introduces deep reinforcement learning and builds a deep Q network that can power autonomous agents.
Chapter 13, Generative Adversarial Networks, explains how to use generative adversarial networks to generate convincing images.