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

In this chapter, we discussed how to use CNNs, which are a type of feed-forward artificial neural network in which the connectivity pattern between neurons is inspired by the organization of an animal's visual cortex. We saw how to cascade a set of layers to construct a CNN and perform different operations in each layer. Then we saw how to train a CNN. Later on, we discussed how to optimize the CNN hyperparameters and optimization.

Finally, we built another CNN, where we utilized all the optimization techniques. Our CNN models did not achieve outstanding accuracy since we iterated both of the CNNs a few times and did not even apply any grid searching techniques; that means we did not hunt for the best combinations of the hyperparameters. Therefore, the takeaway would be to apply more robust feature engineering in the raw images, iterate the training for more...