Book Image

Practical Convolutional Neural Networks

By : Mohit Sewak, Md. Rezaul Karim, Pradeep Pujari
Book Image

Practical Convolutional Neural Networks

By: Mohit Sewak, Md. Rezaul Karim, Pradeep Pujari

Overview of this book

Convolutional Neural Network (CNN) is revolutionizing several application domains such as visual recognition systems, self-driving cars, medical discoveries, innovative eCommerce and more.You will learn to create innovative solutions around image and video analytics to solve complex machine learning and computer vision related problems and implement real-life CNN models. This book starts with an overview of deep neural networkswith the example of image classification and walks you through building your first CNN for human face detector. We will learn to use concepts like transfer learning with CNN, and Auto-Encoders to build very powerful models, even when not much of supervised training data of labeled images is available. Later we build upon the learning achieved to build advanced vision related algorithms for object detection, instance segmentation, generative adversarial networks, image captioning, attention mechanisms for vision, and recurrent models for vision. By the end of this book, you should be ready to implement advanced, effective and efficient CNN models at your professional project or personal initiatives by working on complex image and video datasets.
Table of Contents (11 chapters)

Feature matching

The idea of feature matching is to add an extra variable to the cost function of the generator in order to penalize the difference between absolute errors in the test data and training data.

Semi-supervised classification using a GAN example

In this section, we explain how to use GAN to build a classifier with the semi-supervised learning approach.

In supervised learning, we have a training set of inputs X and class labels y. We train a model that takes X as input and gives y as output.

In semi-supervised learning, our goal is still to train a model that takes X as input and generates y as output. However, not all of our training examples have a label y. ;

We use the SVHN dataset. We'll turn the GAN discriminator into an 11 class discriminator (0 to 9 and one label for the fake image). It will recognize the 10 different classes of real SVHN digits, as well as an eleventh class of fake images that come from the generator. The discriminator will get to train on real labeled images...