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)

Summary


The attention mechanism is the hottest topic in deep learning today and is conceived to be in the center of most of the cutting-edge algorithms under current research, and in probable future applications. Problems such as image captioning, visual question answering, and many more have gotten great solutions by using this approach. In fact, attention is not limited to visual tasks and was conceived earlier for problems such as neural machine translations and other sophisticated NLP problems. Thus, understanding the attention mechanism is vital to mastering many advanced deep learning techniques.

CNNs are used not only for vision but also for many good applications with attention for solving complex NLP problems, such as modeling sentence pairs and machine translation. This chapter covered the attention mechanism and its application to some NLP problems, along with image captioning and recurrent vision models. In RAMs, we did not use CNN; instead, we applied RNN and attention to reduced...