Book Image

Hands-On Deep Learning with TensorFlow

By : Dan Van Boxel
Book Image

Hands-On Deep Learning with TensorFlow

By: Dan Van Boxel

Overview of this book

Dan Van Boxel’s Deep Learning with TensorFlow is based on Dan’s best-selling TensorFlow video course. With deep learning going mainstream, making sense of data and getting accurate results using deep networks is possible. Dan Van Boxel will be your guide to exploring the possibilities with deep learning; he will enable you to understand data like never before. With the efficiency and simplicity of TensorFlow, you will be able to process your data and gain insights that will change how you look at data. With Dan’s guidance, you will dig deeper into the hidden layers of abstraction using raw data. Dan then shows you various complex algorithms for deep learning and various examples that use these deep neural networks. You will also learn how to train your machine to craft new features to make sense of deeper layers of data. In this book, Dan shares his knowledge across topics such as logistic regression, convolutional neural networks, recurrent neural networks, training deep networks, and high level interfaces. With the help of novel practical examples, you will become an ace at advanced multilayer networks, image recognition, and beyond.
Table of Contents (12 chapters)

Chapter 2. Deep Neural Networks

In the previous chapter, we looked at simple TensorFlow operations and how to use logistic regression on our font classification problem. In this chapter, we will dive into one of the most popular and successful machine learning approaches—neural networks. Using TensorFlow, we'll build both simple and deep neural networks to improve our model of the font classification problem. Here, we will put the basics of neural networks into practice. We will also build and train our first neural network with TensorFlow. We will then move on to a neural network with a hidden layer of neurons and understand it completely. When completed, you will have a better grasp of the following topics:

  • Basic neural networks

  • The single hidden layer model

  • The single hidden layer explained

  • The multiple hidden layer model

  • Results of the multiple hidden layer

In our first section, we'll review the basics of neural networks. You will learn common ways to transform input data, understand how neural...