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)

Tensors – multidimensional arrays

Now that we've learned a bit about MNIST digits, we're going to take the time to look at a tensor, and what a tensor is. We're going to be looking at a NumPy of multidimensional arrays. Multidimensional arrays are also called tensors. The math vocabulary can be mildly overwhelming, but we're going to show you that it's a lot simpler than you might think. Then, we'll look at tensor shape. Tensor shape is really the number of dimensions, or, in terms of arrays, the number of different indices that you would use to access them. And then finally, we're going to look at datatypes. The tensors, or multidimensional arrays, can hold a wide array of different datatypes, and we'll explain some of the differences.

Let's start with the basics. The most basic tensor you can imagine is a one tensor, which...