What this book covers
Chapter 1, Getting Started, discusses the techniques and the models we'll apply using TensorFlow. In this chapter, we will install TensorFlow on a machine we can use. After some small steps with basic computations, we will jump into a machine learning problem, successfully building a decent model with just logistic regression and a few lines of TensorFlow code.
Chapter 2, Deep Neural Networks, shows TensorFlow in its prime with deep neural networks. You will learn about the single and multiple hidden layer model. You will also learn about the different types of neural networks and build and train our first neural network with TensorFlow.
Chapter 3, Convolutional Neural Networks, talks about the most powerful developments in deep learning and applies the concepts of convolution to a simple example in TensorFlow. We will tackle the practical aspects of understanding convolution. We will explain what a convolutional and pooling layer is in a neural net, following with a TensorFlow example.
Chapter 4, Introducing Recurrent Neural Networks, introduces the concept of RNN models, and their implementation in TensorFlow. We will look at a simple interface to TensorFlow called TensorFlow learn. We will also walk through dense neural networks as well as understand convolutional neural networks and extracting weights in detail.
Chapter 5, Wrapping Up, wraps up our look at TensorFlow. We'll revisit our TensorFlow models for font classification, and review their accuracy.
