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 3. Convolutional Neural Networks

In the previous chapter we explored deep neural networks, which required ever more parameters to fit. This chapter will guide you through one of the most powerful developments in deep learning and let us use some of our knowledge about the problem space to improve the model. First we're going to explain what a convolutional layer is in a neural net followed by a TensorFlow example. Then we'll do the same for what's called a pooling layer. Finally, we'll adapt our font classification model into a Convolutional Neural Network (CNN) and see how it does.

In this chapter, we will look at the background of convolutional neural nets. We will also implement a convolutional layer in TensorFlow. We will learn max pooling layers and put them into practice and implement a single pooling layer as an example.

At the end of this chapter, you will have great control over the following concepts:

  • Convolutional layer motivation

  • Convolutional layer application

  • Pooling layer...