Contrary to supervised learning, where an algorithm has to associate an input with an output, in reinforcement learning you have another kind of maximization task. You are given an environment (that is, a situation) and you are required to find a solution that will act (something that may require to interact with or even change the environment itself) with the clear purpose of maximizing a resulting reward. Reinforcement learning algorithms, then, are not given any clear, explicit goal but to get the maximum result possible in the end. They are free to find the way to achieve the result by trial and error. This resembles the experience of a toddler who experiments freely in a new environment and analyzes the feedback in order to find out how to get the best from their experience. It also resembles the experience we have with a new video game...
TensorFlow Deep Learning Projects
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TensorFlow Deep Learning Projects
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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.
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TensorFlow Deep Learning Projects
Table of Contents (12 chapters)
Preface
Free Chapter
Recognizing traffic signs using Convnets
Annotating Images with Object Detection API
Caption Generation for Images
Building GANs for Conditional Image Creation
Stock Price Prediction with LSTM
Create and Train Machine Translation Systems
Train and Set up a Chatbot, Able to Discuss Like a Human
Detecting Duplicate Quora Questions
Building a TensorFlow Recommender System
Video Games by Reinforcement Learning
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