Build deep learning and computer vision systems using Python, TensorFlow, Keras, OpenCV, and more, right within the familiar environment of Microsoft Windows. The book starts with an introduction to tools for deep learning and computer vision tasks followed by instructions to install, configure, and troubleshoot them. Here, you will learn how Python can help you build deep learning models on Windows. Moving forward, you will build a deep learning model and understand the internal-workings of a convolutional neural network on Windows. Further, you will go through different ways to visualize the internal-workings of deep learning models along with an understanding of transfer learning where you will learn how to build model architecture and use data augmentations. Next, you will manage and train deep learning models on Windows before deploying your application as a web application. You’ll also do some simple image processing and work with computer vision options that will help you build various applications with deep learning. Finally, you will use generative adversarial networks along with reinforcement learning. After reading __Deep Learning on Windows__, you will be able to design deep learning models and web applications on the Windows operating system. **What You Will Learn** * Understand the basics of Deep Learning and its history Get Deep Learning tools working on Microsoft Windows* Understand the internal-workings of Deep Learning models by using model visualization techniques, such as the built-in plot\_model function of Keras and third-party visualization tools * Understand Transfer Learning and how to utilize it to tackle small datasets * Build robust training scripts to handle long-running training jobs * Convert your Deep Learning model into a web application * Generate handwritten digits and human faces with DCGAN (Deep Convolutional Generative Adversarial Network) * Understand the basics of Reinforcement Learning **Who This Book Is For** AI developers and enthusiasts wanting to work on the Windows platform. Front Matter ....Pages i-xviii What Is Deep Learning? (Thimira Amaratunga)....Pages 1-14 Where to Start Your Deep Learning (Thimira Amaratunga)....Pages 15-31 Setting Up Your Tools (Thimira Amaratunga)....Pages 33-66 Building Your First Deep Learning Model (Thimira Amaratunga)....Pages 67-100 Understanding What We Built (Thimira Amaratunga)....Pages 101-114 Visualizing Models (Thimira Amaratunga)....Pages 115-130 Transfer Learning (Thimira Amaratunga)....Pages 131-179 Starting, Stopping, and Resuming Learning (Thimira Amaratunga)....Pages 181-213 Deploying Your Model as a Web Application (Thimira Amaratunga)....Pages 215-231 Having Fun with Computer Vision (Thimira Amaratunga)....Pages 233-251 Introduction to Generative Adversarial Networks (Thimira Amaratunga)....Pages 253-286 Basics of Reinforcement Learning (Thimira Amaratunga)....Pages 287-310 Back Matter ....Pages 311-338
