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)

Comparison between classical dense neural networks

In this section, we'll be looking at the actual structure of a classical or dense neural network. We'll start off with a sample neural network structure, and then we'll expand that to build a visualization of the network that you would need in order to understand the MNIST digits. Then, finally, we'll learn how the tensor data is actually inserted into a network.

Let's start by looking at the structure of a dense neural network. Using the network package, we will draw a picture of a neural network. The following screenshot shows the three layers that we are setting up—an input layer, an activation layer, and then an output layer—and fully connecting them:

Neural network with three layers

That's what these two loops in the middle are doing. They are putting an edge between every input...