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)


In 2010, a challenge from ImageNet (known as ILSVRC 2010) came out with a CNN architecture, LeNet 5, built by Yann Lecun. This network takes a 32 x 32 image as input, which goes to the convolution layers (C1) and then to the subsampling layer (S2). Today, the subsampling layer is replaced by a pooling layer. Then, there is another sequence of convolution layers (C3) followed by a pooling (that is, subsampling) layer (S4). Finally, there are three fully connected layers, including the OUTPUT layer at the end. This network was used for zip code recognition in post offices. Since then, every year various CNN architectures were introduced with the help of this competition:

LeNet 5 – CNN architecture from Yann Lecun's article in 1998

Therefore, we can conclude the following points:

  • The input to this network is a grayscale 32 x 32 image
  • The architecture...