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Python Deep Learning

Python Deep Learning

By : Zocca, Spacagna, Daniel Slater, Roelants
4.1 (10)
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Python Deep Learning

Python Deep Learning

4.1 (10)
By: Zocca, Spacagna, Daniel Slater, Roelants

Overview of this book

With an increasing interest in AI around the world, deep learning has attracted a great deal of public attention. Every day, deep learning algorithms are used broadly across different industries. The book will give you all the practical information available on the subject, including the best practices, using real-world use cases. You will learn to recognize and extract information to increase predictive accuracy and optimize results. Starting with a quick recap of important machine learning concepts, the book will delve straight into deep learning principles using Sci-kit learn. Moving ahead, you will learn to use the latest open source libraries such as Theano, Keras, Google's TensorFlow, and H20. Use this guide to uncover the difficulties of pattern recognition, scaling data with greater accuracy and discussing deep learning algorithms and techniques. Whether you want to dive deeper into Deep Learning, or want to investigate how to get more out of this powerful technology, you’ll find everything inside.
Table of Contents (12 chapters)
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11
Index

Model-based approaches


The approaches we've so far shown can do a good job of learning all kinds of tasks, but an agent trained in these ways can still suffer from significant limitations:

  • It trains very slowly; a human can learn a game like Pong from a couple of plays, while for Q-learning, it may take millions of playthroughs to get to a similar level.

  • For games that require long-term planning, all the techniques perform very badly. Imagine a platform game where a player must retrieve a key from one side of a room to open a door on the other side. There will rarely be a passage of play where this occurs, and even then, the chance of learning that it was the key that lead to the extra reward from the door is miniscule.

  • It cannot formulate a strategy or in any way adapt to a novel opponent. It may do well against an opponent it trains against, but when presented with an opponent showing some novelty in play, it will take a long time to learn to adapt to this.

  • If given a new goal within an environment...

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