Mohit Sewak, Md. Rezaul Karim, Pradeep Pujari. Practical Convolutional Neural Networks

Packt Publishing, 2018. — 218 p. — ISBN: 978-1-78839-230-3.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.Deep Neural Networks – Overview
Building blocks of a neural network
Introduction to TensorFlow
Introduction to the MNIST dataset
Keras deep learning library overview
Handwritten number recognition with Keras and MNIST
Understanding backpropagationIntroduction to Convolutional Neural Networks
History of CNNs
Convolutional neural networks
Practical example – image classificationBuild Your First CNN and Performance Optimization
CNN architectures and drawbacks of DNNs
Convolution and pooling operations in TensorFlow
Training a CNN
Building, training, and evaluating our first CNN
Model performance optimizationPopular CNN Model Architectures
Introduction to ImageNet
LeNet
AlexNet architecture
VGGNet architecture
GoogLeNet architecture
ResNet architectureTransfer Learning
Feature extraction approach
Transfer learning example
Multi-task learningAutoencoders for CNN
Introducing to autoencoders
Convolutional autoencoder
Applications
An example of compressionObject Detection and Instance Segmentation with CNN
The differences between object detection and image classification
Traditional, nonCNN approaches to object detection
R-CNN – Regions with CNN features
Fast R-CNN – fast region-based CNN
Faster R-CNN – faster region proposal network-based CNN
Mask R-CNN – Instance segmentation with CNN
Instance segmentation in codeGAN: Generating New Images with CNN
Pix2pix - Image-to-Image translation GAN
GAN – code example
Feature matchingAttention Mechanism for CNN and Visual Models
Attention mechanism for image captioning
Types of Attention
Using attention to improve visual modelsOther Books You May Enjoy