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)

Training a CNN

In the previous section, we have seen how to construct a CNN and apply different operations on its different layers. Now when it comes to training a CNN, it is much trickier as it needs a lot of considerations to control those operations such as applying appropriate activation function, weight and bias initialization, and of course, using optimizers intelligently.

There are also some advanced considerations such as hyperparameter tuning for optimized too. However, that will be discussed in the next section. We first start our discussion with weight and bias initialization.

Weight and bias initialization

One of the most common initialization techniques in training a DNN is random initialization. The idea...