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)

Traditional, nonCNN approaches to object detection


Libraries such as OpenCV and some others saw rapid inclusion in the software bundles for Smartphones, Robotic projects, and many others, to provide detection capabilities of specific objects (face, smile, and so on), and Computer Vision like benefits, though with some constraints even before the prolific adoption of CNN.

CNN-based research in this area of object detection and Instance Segmentation provided many advancements and performance enhancements to this field, not only enabling large-scale deployment of these systems but also opening avenues for many new solutions. But before we plan to jump into CNN based advancements, it will be a good idea to understand how the challenges cited in the earlier section were answered to make object detection possible in the first place (even with all the constraints), and then we will logically start our discussion about the different researchers and the application of CNN to solve other problems that...