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)

The TensorFlow object detection API

As a way of boosting the capabilities of the research community, Google research scientists and software engineers often develop state-of-the-art models and make them available to the public instead of keeping them proprietary. As described in the Google research blog post, https://research.googleblog.com/2017/06/supercharge-your-computer-vision-models.html , on October 2016, Google's in-house object detection system placed first in the COCO detection challenge, which is focused on finding objects in images (estimating the chance that an object is in this position) and their bounding boxes (you can read the technical details of their solution at https://arxiv.org/abs/1611.10012).

The Google solution has not only contributed to quite a few papers and been put to work in some Google products (Nest Cam - https://nest.com/cameras/nest-aware...