Book Image

Hands-On Deep Learning for Images with TensorFlow

By : Will Ballard
Book Image

Hands-On Deep Learning for Images with TensorFlow

By: Will Ballard

Overview of this book

TensorFlow is Google’s popular offering for machine learning and deep learning, quickly becoming a favorite tool for performing fast, efficient, and accurate deep learning tasks. Hands-On Deep Learning for Images with TensorFlow shows you the practical implementations of real-world projects, teaching you how to leverage TensorFlow’s capabilities to perform efficient image processing using the power of deep learning. With the help of this book, you will get to grips with the different paradigms of performing deep learning such as deep neural nets and convolutional neural networks, followed by understanding how they can be implemented using TensorFlow. By the end of this book, you will have mastered all the concepts of deep learning and their implementation with TensorFlow and Keras.
Table of Contents (7 chapters)


In this section, we will learn about convolutions. We're going to see the structure of a convolutional network, and then we're going to apply that to two dimensions, just like we would if we were using it for an image. Finally, we're going to discuss the benefits of a convolutional network and why you would choose to use one.

Alright, let's get started! First, we're going to import the networkx packages and matplotlib, just like we did for the classical neural network:

Importing packages

The code here is similar to what we learned in the previous chapter, but there's a minor change:

Connecting from the inputs to the activation

You will have noticed that where we are connecting from the inputs to the activation, rather than connecting every input to every activation, we have a window. In this case, we're using a window of three...