Book Image

Hands-On Convolutional Neural Networks with TensorFlow

By : Iffat Zafar, Giounona Tzanidou, Richard Burton, Nimesh Patel, Leonardo Araujo
Book Image

Hands-On Convolutional Neural Networks with TensorFlow

By: Iffat Zafar, Giounona Tzanidou, Richard Burton, Nimesh Patel, Leonardo Araujo

Overview of this book

Convolutional Neural Networks (CNN) are one of the most popular architectures used in computer vision apps. This book is an introduction to CNNs through solving real-world problems in deep learning while teaching you their implementation in popular Python library - TensorFlow. By the end of the book, you will be training CNNs in no time! We start with an overview of popular machine learning and deep learning models, and then get you set up with a TensorFlow development environment. This environment is the basis for implementing and training deep learning models in later chapters. Then, you will use Convolutional Neural Networks to work on problems such as image classification, object detection, and semantic segmentation. After that, you will use transfer learning to see how these models can solve other deep learning problems. You will also get a taste of implementing generative models such as autoencoders and generative adversarial networks. Later on, you will see useful tips on machine learning best practices and troubleshooting. Finally, you will learn how to apply your models on large datasets of millions of images.
Table of Contents (17 chapters)
Title Page
Copyright and Credits
Packt Upsell

Convolutional neural networks

We will now look at another type of neural network that is especially designed to work with data that has some spatial properties, such as images. This type of neural network is called a Convolutional Neural Network (CNN).

A CNN is mainly composed of layers called convolution layers that filter their layer inputs to find useful features within those inputs. This filtering operation is called convolution, which gives rise to the name of this kind of neural network.

The following diagram shows the 2-D convolution operation on an image and its result. It is important to remember that the filter kernel has a depth that matches the depth of the input (3 in this case):

It is also important to be clear that an input to a convolution layer doesn't have to be a 1 or 3 channel image. Input tensors to a convolution layer can have any amount of channels.


A lot of the time when talking about convolution layers in a CNN people like to shorten the word convolution down to...