Practical Convolutional Neural Networks : Implement Advanced Deep Learning Models Using Python

**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 DescriptionConvolutional 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 ForThis 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 Contents1. 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 Cover Title Page Copyright and Credits Packt Upsell Contributors Table of Contents Preface Chapter 1: Deep Neural Networks – Overview Building blocks of a neural network Introduction to TensorFlow Installing TensorFlow For macOS X/Linux variants TensorFlow basics Basic math with TensorFlow Softmax in TensorFlow Introduction to the MNIST dataset The simplest artificial neural network Building a single-layer neural network with TensorFlow Keras deep learning library overview Layers in the Keras model Handwritten number recognition with Keras and MNIST Retrieving training and test data Flattened data Visualizing the training data Building the network Training the network Testing Understanding backpropagation Summary Chapter 2: Introduction to Convolutional Neural Networks History of CNNs Convolutional neural networks How do computers interpret images? Code for visualizing an image Dropout Input layer Convolutional layer Convolutional layers in Keras Pooling layer Practical example – image classification Image augmentation Summary Chapter 3: Build Your First CNN and Performance Optimization CNN architectures and drawbacks of DNNs Convolutional operations Pooling, stride, and padding operations Fully connected layer Convolution and pooling operations in TensorFlow Applying pooling operations in TensorFlow Convolution operations in TensorFlow Training a CNN Weight and bias initialization Regularization Activation functions Using sigmoid Using tanh Using ReLU Building, training, and evaluating our first CNN Dataset description Step 1 – Loading the required packages Step 2 – Loading the training/test images to generate train/test set Step 3- Defining CNN hyperparameters Step 4 – Constructing the CNN layers Step 5 – Preparing the TensorFlow graph Step 6 – Creating a CNN model Step 7 – Running the TensorFlow graph to train the CNN model Step 8 – Model evaluation Model performance optimization Number of hidden layers Number of neurons per hidden layer Batch normalization Advanced regularization and avoiding overfitting Applying dropout operations with TensorFlow Which optimizer to use? Memory tuning Appropriate layer placement Building the second CNN by putting everything together Dataset description and preprocessing Creating the CNN model Training and evaluating the network Summary Chapter 4: Popular CNN Model Architectures Introduction to ImageNet LeNet AlexNet architecture Traffic sign classifiers using AlexNet VGGNet architecture VGG16 image classification code example GoogLeNet architecture Architecture insights Inception module ResNet architecture Summary Chapter 5: Transfer Learning Feature extraction approach Target dataset is small and is similar to the original training dataset Target dataset is small but different from the original training dataset Target dataset is large and similar to the original training dataset Target dataset is large and different from the original training dataset Transfer learning example Multi-task learning Summary Chapter 6: Autoencoders for CNN Introducing to autoencoders Convolutional autoencoder Applications An example of compression Summary Chapter 7: Object Detection and Instance Segmentation with CNN The differences between object detection and image classification Why is object detection much more challenging than image classification? Traditional, nonCNN approaches to object detection Haar features, cascading classifiers, and the Viola-Jones algorithm Haar Features Cascading classifiers The Viola-Jones algorithm 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 code Creating the environment Installing Python dependencies (Python2 environment) Downloading and installing the COCO API and detectron library (OS shell commands) Preparing the COCO dataset folder structure Running the pre-trained model on the COCO dataset References Summary Chapter 8: GAN: Generating New Images with CNN Pix2pix - Image-to-Image translation GAN CycleGAN Training a GAN model GAN – code example Calculating loss Adding the optimizer Semi-supervised learning and GAN Feature matching Semi-supervised classification using a GAN example Deep convolutional GAN Batch normalization Summary Chapter 9: Attention Mechanism for CNN and Visual Models Attention mechanism for image captioning Types of Attention Hard Attention Soft Attention Using attention to improve visual models Reasons for sub-optimal performance of visual CNN models Recurrent models of visual attention Applying the RAM on a noisy MNIST sample Glimpse Sensor in code References Summary Other Books You May Enjoy Index One stop guide to implementing award-winning, and cutting-edge CNN architectures About This Book 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, 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. 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 In Detail 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 .. This book helps you master CNN, from the basics to the most advanced concepts in CNN such as GANs, instance classification and attention mechanism for vision models and more. You will implement advanced CNN models using complex image and video datasets. By the end of the book you will learn CNN's best practices to implement smart ConvNet ..