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)


Autoencoders are used for dimensionality reduction, or data compression, and image denoising. Dimensionality reduction, in turn, helps in improving runtime performance and consumes less memory. An image search can become highly efficient in low-dimension spaces.

An example of compression

The Network architecture comprises of an encoder network, which is a typical convolutional pyramid. Each convolutional layer is followed by a max-pooling layer; this reduces the dimensions of the layers. 

The decoder converts the input from a sparse representation to a wide reconstructed image. A schematic of the network is shown here:

The encoder layer output image size is 4 x 4 x 8 = 128. The original image size was...