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)

Introduction to Convolutional Neural Networks

Convolutional Neural Networks (CNNs) are everywhere. In the last five years, we have seen a dramatic rise in the performance of visual recognition systems due to the introduction of deep architectures for feature learning and classification. CNNs have achieved good performance in a variety of areas, such as automatic speech understanding, computer vision, language translation, self-driving cars, and games such as Alpha Go. Thus, the applications of CNNs are almost limitless. DeepMind (from Google) recently published WaveNet, which uses a CNN to generate speech that mimics any human voice (

In this chapter, we will cover the following topics:

  • History of CNNs
  • Overview of a CNN
  • Image augmentation