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


The first generative model we will look at is the autoencoder model. An autoencoder is a simple neural network that is composed of two parts: an encoder and a decoder. The idea is that the encoder part will compress your input into a smaller dimension. From this smaller dimension, it then tries to reconstruct the input using the decoder part of the model. This smaller dimension is often called by many names such as latent space, hidden space, an embedding, or a coding.

If the autoencoder is able to reproduce its input, then, in theory, this latent space should encode all the important information needed to represent the original data, but with the advantage of being a smaller dimension than the input. The encoder can be thought of as a way of compressing the input data while the decoder is the way to uncompress it. We can see what a simple autoencoder looks like in the following illustration. Our latent space or coding is the part in the middle labeled z.

Traditionally, in an...