Practical Convolutional Neural Networks: Implement advanced deep learning models using Python

by Mohit Sewak, Md. Rezaul Karim, Pradeep Pujari

One stop guide to implementing award-winning, and cutting-edge CNN architectures 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. This book is for data scientists, machine learning and deep learning practitioners, Cognitive and Artificial Intelligence enthusiasts who want to move one step further in building Convolutional Neural Networks. Get hands-on experience with extreme datasets and different CNN architectures to build efficient and smart ConvNet models. Basic knowledge of deep learning concepts and Python programming language is expected.

218 pages, Paperback

Published February 27, 2018

Reviews

[Yanwei Liu · Rating 5 out of 5]

A very detailed book in CNN, it introduces the basic usage of TensorFlow and the must-know knowledge in Convolutional Neural Networks.

After a brief introduction, it starts with the application of Image Classification with TensorFlow and some best practice tricks in CNN. and the four different situations for Transfer Learning is also well-explained in this book.

By the way, in the final chapter of this book, it teaches us the concept of Attention Mechanism in CNN, bridges my gap to Attention.

I recommend this book for Computer Vision beginner.

[Benjamin · Rating 2 out of 5]

Informative but pretty hard to follow at times. I wish they had more practical tips and didn’t gloss over so much.