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
Contributors
Preface
Index

Artificial neural networks


Very vaguely inspired by the biological network of neurons residing in our brain, artificial neural networks (ANNs) are made up of a collection of units named artificial neurons that are organized into the following three types of layers:

  • Input layer
  • Hidden layer
  • Output layer

The basic artificial neuron works (see the following image) by calculating a dot product between an input and its internal weights, and the results is then passed to a nonlinear activation function f (sigmoid, in this example). These artificial neurons are then connected together to form a network. During the training of this network, the aim is to find the proper set of weights that will help with whatever task we want our network to do:

 

Next, we have an example of a 2-layerfeed forward artificial neural network. Imagine that the connections between neurons are the weights that will be learned during training. In this example, Layer L1 will be the input layer, L2 the hidden layer, and L3 the...