Book Image

TensorFlow Deep Learning Projects

By : Alexey Grigorev, Rajalingappaa Shanmugamani
Book Image

TensorFlow Deep Learning Projects

By: Alexey Grigorev, Rajalingappaa Shanmugamani

Overview of this book

TensorFlow is one of the most popular frameworks used for machine learning and, more recently, deep learning. It provides a fast and efficient framework for training different kinds of deep learning models, with very high accuracy. This book is your guide to master deep learning with TensorFlow with the help of 10 real-world projects. TensorFlow Deep Learning Projects starts with setting up the right TensorFlow environment for deep learning. You'll learn how to train different types of deep learning models using TensorFlow, including Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, and Generative Adversarial Networks. While doing this, you will build end-to-end deep learning solutions to tackle different real-world problems in image processing, recommendation systems, stock prediction, and building chatbots, to name a few. You will also develop systems that perform machine translation and use reinforcement learning techniques to play games. By the end of this book, you will have mastered all the concepts of deep learning and their implementation with TensorFlow, and will be able to build and train your own deep learning models with TensorFlow confidently.
Table of Contents (12 chapters)

Annotating Images with Object Detection API

Computer vision has made great leaps forward in recent years because of deep learning, thus granting computers a higher grade in understanding visual scenes. The potentialities of deep learning in vision tasks are great: allowing a computer to visually perceive and understand its surroundings is a capability that opens the door to new artificial intelligence applications in both mobility (for instance, self-driving cars can detect if an appearing obstacle is a pedestrian, an animal or another vehicle from the camera mounted on the car and decide the correct course of action) and human-machine interaction in everyday-life contexts (for instance, allowing a robot to perceive surrounding objects and successfully interact with them).

After presenting ConvNets and how they operate in the first chapter, we now intend to create a quick, easy...