Practical Convolutional Neural Network Models

One stop guide to implementing award-winning, and cutting-edge CNN architectures

Key Features

• Fast-paced guide with use cases and real-world examples to get well versed with CNN techniques
• Implement CNN models on image classification, transfer learning, Object Detection, Instance Segmentation, GANs and more
• Implement powerful use-cases like image captioning, reinforcement learning for hard attention, and recurrent attention models

Book Description

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.

What you will learn

• From CNN basic building blocks to advanced concepts understand practical areas they can be applied to
• Build an image classifier CNN model to understand how different components interact with each other, and then learn how to optimize it
• Learn different algorithms that can be applied to Object Detection, and Instance Segmentation
• Learn advanced concepts like attention mechanisms for CNN to improve prediction accuracy
• Understand transfer learning and implement award-winning CNN architectures like AlexNet, VGG, GoogLeNet, ResNet and more
• Understand the working of generative adversarial networks and how it can create new, unseen images

Who This Book Is For

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.

Table of Contents

1. Deep Neural Networks - Overview
2. Introduction to Convolutional Neural Networks
3. Build Your First CNN and Performance Optimization
4. Popular CNN Model's Architectures
5. Transfer Learning
6. Autoencoders for CNN
7. Object Detection with CNN
8. Generative Adversarial Network
9. Visual Attention Based CNN

Mohit Sewak is a Sr. Cognitive Data Scientist with IBM, and a Ph.D. scholar in AI & CS with BITS Pilani. He holds several Patents and Publications in AI, Deep Learning, and Machine Learning. He has been the Lead Data Scientist for some of the very successful global AI/ ML software and Industry solutions and had been earlier engaged with solutioning and research for Watson Cognitive Commerce product line. He has 14 years of very rich experience in architecting and solutioning with technologies like TensorFlow, Torch, Caffe, Theano, Keras, Watson and others.

Md. Rezaul Karim is a Research Scientist at Fraunhofer FIT, Germany. He is also a PhD candidate at RWTH Aachen University, Germany. Before joining FIT, he worked as a Researcher at the Insight Centre for Data Analytics, Ireland. Earlier, he worked as a Lead Engineer at Samsung Electronics, Korea.

He has 9 years of R&D experience with C++, Java, R, Scala, and Python. He has published several research papers concerning bioinformatics, big data, and deep learning. He has practical working experience with Spark, Zeppelin, Hadoop, Keras, Scikit-Learn, TensorFlow, DeepLearning4j, MXNet, and H2O.

Pradeep Pujari is machine learning engineer at Walmart Labs and distinguished member of ACM. His core domain expertise is in information retrieval, machine learning and natural language processing. In off hours, he loves exploring AI technologies, enjoys reading and mentoring.