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Python Deep Learning Projects

You're reading from   Python Deep Learning Projects 9 projects demystifying neural network and deep learning models for building intelligent systems

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Product type Paperback
Published in Oct 2018
Publisher Packt
ISBN-13 9781788997096
Length 472 pages
Edition 1st Edition
Languages
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Authors (3):
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Rahul Kumar Rahul Kumar
Author Profile Icon Rahul Kumar
Rahul Kumar
Matthew Lamons Matthew Lamons
Author Profile Icon Matthew Lamons
Matthew Lamons
Abhishek Nagaraja Abhishek Nagaraja
Author Profile Icon Abhishek Nagaraja
Abhishek Nagaraja
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Toc

Table of Contents (17) Chapters Close

Preface 1. Building Deep Learning Environments FREE CHAPTER 2. Training NN for Prediction Using Regression 3. Word Representation Using word2vec 4. Building an NLP Pipeline for Building Chatbots 5. Sequence-to-Sequence Models for Building Chatbots 6. Generative Language Model for Content Creation 7. Building Speech Recognition with DeepSpeech2 8. Handwritten Digits Classification Using ConvNets 9. Object Detection Using OpenCV and TensorFlow 10. Building Face Recognition Using FaceNet 11. Automated Image Captioning 12. Pose Estimation on 3D models Using ConvNets 13. Image Translation Using GANs for Style Transfer 14. Develop an Autonomous Agent with Deep R Learning 15. Summary and Next Steps in Your Deep Learning Career 16. Other Books You May Enjoy

Develop an Autonomous Agent with Deep R Learning

Welcome to the chapter on reinforcement learning. In the previous chapters, we have worked on solving supervised learning problems. In this chapter, we will learn to build and train a deep reinforcement learning model capable of playing games.

Reinforcement learning is often a new paradigm for deep learning engineers and this is why we're using the framework of a game for this training. The business use cases that we should be looking out for are typified by process optimization. Reinforcement learning is great for gaming, but also applicable in use cases ranging from drone control (https://arxiv.org/pdf/1707.05110.pdf) and navigation to optimizing file downloads over mobile networks (http://anrg.usc.edu/www/papers/comsnets_2017.pdf).

We will do this with something called deep Q-learning and deep State-Action-Reward-State...

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