Book Image

TensorFlow Machine Learning Cookbook - Second Edition

By : Nick McClure, Sujit Pal
Book Image

TensorFlow Machine Learning Cookbook - Second Edition

By: Nick McClure, Sujit Pal

Overview of this book

TensorFlow is an open source software library for Machine Intelligence. The independent recipes in this book will teach you how to use TensorFlow for complex data computations and allow you to dig deeper and gain more insights into your data than ever before. With the help of this book, you will work with recipes for training models, model evaluation, sentiment analysis, regression analysis, clustering analysis, artificial neural networks, and more. You will explore RNNs, CNNs, GANs, reinforcement learning, and capsule networks, each using Google's machine learning library, TensorFlow. Through real-world examples, you will get hands-on experience with linear regression techniques with TensorFlow. Once you are familiar and comfortable with the TensorFlow ecosystem, you will be shown how to take it to production. By the end of the book, you will be proficient in the field of machine intelligence using TensorFlow. You will also have good insight into deep learning and be capable of implementing machine learning algorithms in real-world scenarios.
Table of Contents (13 chapters)

Introduction

In mathematics, a convolution is a function that is applied over the output of another function. In our case, we will consider applying a matrix multiplication (filter) across an image. For our purposes, we consider an image to be a matrix of numbers. These numbers may represent pixels or even image attributes. The convolution operation we will apply to these matrices involves moving a filter of fixed width across the image and applying element-wise multiplication to get our result.

See the following diagram for a conceptual understanding of how image convolution can work:

Figure 1: How a convolutional filter applied across an image (length by width by depth) operates to create a new feature layer. Here, we have a 2x2 convolutional filter, operating in the valid spaces of the 5x5 input with a stride of 1 in both directions. The result is a 4x4 matrix

CNNs also have...