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)

Deep Neural Networks – Overview

In the past few years, we have seen remarkable progress in the field of AI (deep learning). Today, deep learning is the cornerstone of many advanced technological applications, from self-driving cars to generating art and music. Scientists aim to help computers to not only understand speech but also speak in natural languages. Deep learning is a kind of machine learning method that is based on learning data representation as opposed to task-specific algorithms. Deep learning enables the computer to build complex concepts from simpler and smaller concepts. For example, a deep learning system recognizes the image of a person by combining lower label edges and corners and combines them into parts of the body in a hierarchical way. The day is not so far away when deep learning will be extended to applications that enable machines to think on their own.

In this chapter, we will cover the following topics:

  • Building blocks of a neural network
  • Introduction to TensorFlow
  • Introduction to Keras
  • Backpropagation