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Statistics for Machine Learning

You're reading from   Statistics for Machine Learning Techniques for exploring supervised, unsupervised, and reinforcement learning models with Python and R

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Product type Paperback
Published in Jul 2017
Publisher Packt
ISBN-13 9781788295758
Length 442 pages
Edition 1st Edition
Languages
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Author (1):
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Pratap Dangeti Pratap Dangeti
Author Profile Icon Pratap Dangeti
Pratap Dangeti
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Table of Contents (10) Chapters Close

Preface 1. Journey from Statistics to Machine Learning FREE CHAPTER 2. Parallelism of Statistics and Machine Learning 3. Logistic Regression Versus Random Forest 4. Tree-Based Machine Learning Models 5. K-Nearest Neighbors and Naive Bayes 6. Support Vector Machines and Neural Networks 7. Recommendation Engines 8. Unsupervised Learning 9. Reinforcement Learning

Applications of reinforcement learning with integration of machine learning and deep learning


Reinforcement learning combined with machine learning, or deep learning, has created state-of-the-art artificial intelligence solutions for various cutting-edge problems in recent times. A complete explanation with code examples is beyond the scope of this book, but we will give you a high-level view of what is inside these technologies. The following are the most popular and known recent trends in this field, but the applications are not just restricted to these:

  • Automotive vehicle control (self-driving cars)
  • Google DeepMind AlphaGo for playing Go games
  • Robotics (with a soccer example)

Automotive vehicle control - self-driving cars

Self-driving cars are the new trend in the industry and many tech giants are working in this area now. Deep learning technologies, like convolutional neural networks, are used to learn Q-functions which control the actions, like moving forward, backward, taking left and right...

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