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)

Turning images into tensors

In the previous section, we learned a bit about what a tensor is. Now, we're going to use that knowledge to prepare image data as tensors for machine learning. First, we'll ask a question: why are we working with data in floating points? Then, we will learn the difference between samples and the data points at the end of them. Finally, we will normalize the data for use in machine learning.

So, why a floating point? Well, the real reason is that machine learning is fundamentally a math optimization problem, and when we're working with floating points, the computer is trying to optimize a series of mathematical relationships to find learned functions that can then predict outputs. So, preparing our data for machine learning does involve reformatting normal binary data, such as an image, into a series of floating point numbers, which isn...