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.