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)

Image Data

In the previous chapter, we prepared our Machine Learning Toolkit, where we set up Keras and Docker in order to allow us to run Jupyter Notebooks to process machine learning.

In this chapter, we're going to look into preparing image data for use with machine learning and the steps that are involved in hooking that into Keras. We're going to start by learning about the MNIST digits. These are handwritten characters in the form of images that we're effectively going to perform Optical Character Recognition (OCR) on with machine learning. Then, we're going to talk about tensors. Tensors sounds like a math word, and it is really, but as a programmer, you've seen multidimensional arrays, so you've actually already been using tensors, and I'll show you the equivalency. Afterward, we're going to turn images into tensors. Images, as you...