Reinforcement learning is a type of machine learning in which an agent learns from the environment. The agent takes actions and, as a result of the actions, the environment returns observations and rewards. From the observation and rewards, the agent learns the policy and takes further actions, thus continuing the sequence of actions, observations, and rewards. In the long run, the agent has to learn the policy such that, when it takes actions based on the policy, it does so in such a way as to maximize the long-term rewards.
TensorFlow Machine Learning Projects
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TensorFlow Machine Learning Projects
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Overview of this book
TensorFlow has transformed the way machine learning is perceived. TensorFlow Machine Learning Projects teaches you how to exploit the benefits—simplicity, efficiency, and flexibility—of using TensorFlow in various real-world projects. With the help of this book, you’ll not only learn how to build advanced projects using different datasets but also be able to tackle common challenges using a range of libraries from the TensorFlow ecosystem.
To start with, you’ll get to grips with using TensorFlow for machine learning projects; you’ll explore a wide range of projects using TensorForest and TensorBoard for detecting exoplanets, TensorFlow.js for sentiment analysis, and TensorFlow Lite for digit classification.
As you make your way through the book, you’ll build projects in various real-world domains, incorporating natural language processing (NLP), the Gaussian process, autoencoders, recommender systems, and Bayesian neural networks, along with trending areas such as Generative Adversarial Networks (GANs), capsule networks, and reinforcement learning. You’ll learn how to use the TensorFlow on Spark API and GPU-accelerated computing with TensorFlow to detect objects, followed by how to train and develop a recurrent neural network (RNN) model to generate book scripts.
By the end of this book, you’ll have gained the required expertise to build full-fledged machine learning projects at work.
Table of Contents (23 chapters)
Title Page
Copyright and Credits
Dedication
About Packt
Contributors
Preface
Free Chapter
Overview of TensorFlow and Machine Learning
Using Machine Learning to Detect Exoplanets in Outer Space
Sentiment Analysis in Your Browser Using TensorFlow.js
Digit Classification Using TensorFlow Lite
Speech to Text and Topic Extraction Using NLP
Predicting Stock Prices using Gaussian Process Regression
Credit Card Fraud Detection using Autoencoders
Generating Uncertainty in Traffic Signs Classifier Using Bayesian Neural Networks
Generating Matching Shoe Bags from Shoe Images Using DiscoGANs
Classifying Clothing Images using Capsule Networks
Making Quality Product Recommendations Using TensorFlow
Object Detection at a Large Scale with TensorFlow
Generating Book Scripts Using LSTMs
Playing Pacman Using Deep Reinforcement Learning
What is Next?
Other Books You May Enjoy
Index
Customer Reviews