Practical Convolutional Neural Network

<Practical Convolutional Neural Network> ▶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

▶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

▶Who This Book Is For
This book is for data scientists, machine learning, and deep learning practitioners, and cognitive and artificial intelligence enthusiasts who want to move one step further in building CNNs. 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.

▶What this book covers
⦁ Chapter 1, Deep Neural Networks - Overview, it gives a quick refresher of the science of deep neural networks and different frameworks that can be used to implement such networks, with the mathematics behind them.
⦁ Chapter 2, Introduction to Convolutional Neural Networks, it introduces the readers to convolutional neural networks and shows how deep learning can be used to extract insights from images.
⦁ Chapter 3, Build Your First CNN and Performance Optimization, constructs a simple CNN model for image classification from scratch, and explains how to tune hyperparameters and optimize training time and performance of CNNs for improved efficiency and accuracy
respectively.
⦁ Chapter 4, Popular CNN Model Architectures, shows the advantages and working of different popular (and award winning) CNN architectures, how they differ from each other, and how to use them.
⦁ Chapter 5, Transfer Learning, teaches you to take an existing pretrained network and adapt it to a new and different dataset. There is also a custom classification problem for a real-life application using a technique called transfer learning.
⦁ Chapter 6, Autoencoders for CNN, introduces an unsupervised learning technique called autoencoders. We walk through different applications of autoencoders for CNN, such as image compression.
⦁ Chapter 7, Object Detection and Instance Segmentation with CNN, teaches the difference between object detection, instance segmentation, and image classification. We then learn multiple techniques for object detection and instance segmentation with CNNs.
⦁ Chapter 8, GAN—.Generating New Images with CNN, explores generative CNN Networks, and then we combine them with our learned discriminative CNN networks to create new images with CNN/GAN.
⦁ Chapter 9, Attention Mechanism for CNN and Visual Models, teaches the intuition behind attention in deep learning and learn how attention-based models are used to implement some advanced solutions (image captioning and RAM). We also understand the different types of attention and the role of reinforcement learning with respect to the hard attention mechanism.