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Keras Reinforcement Learning Projects

You're reading from   Keras Reinforcement Learning Projects 9 projects exploring popular reinforcement learning techniques to build self-learning agents

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Product type Paperback
Published in Sep 2018
Publisher Packt
ISBN-13 9781789342093
Length 288 pages
Edition 1st Edition
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Author (1):
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Giuseppe Ciaburro Giuseppe Ciaburro
Author Profile Icon Giuseppe Ciaburro
Giuseppe Ciaburro
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Table of Contents (13) Chapters Close

Preface 1. Overview of Keras Reinforcement Learning FREE CHAPTER 2. Simulating Random Walks 3. Optimal Portfolio Selection 4. Forecasting Stock Market Prices 5. Delivery Vehicle Routing Application 6. Continuous Balancing of a Rotating Mechanical System 7. Dynamic Modeling of a Segway as an Inverted Pendulum System 8. Robot Control System Using Deep Reinforcement Learning 9. Handwritten Digit Recognizer 10. Playing the Board Game Go 11. What's Next? 12. Other Books You May Enjoy

Handwritten digit recognition using an autoencoder

An autoencoder is a neural network whose purpose is to code its input in small size. The result obtained will then be used to reconstruct the input itself. Autoencoders are made up of the union of the following two subnets:

  • Encoder, which calculates the z = ϕ(x) function, given an x input, the encoder encodes it in a z variable, also called latent variable. The z variable usually has much smaller dimensions than x.
  • Decoder, which calculates the x' = ψ(z) function.

Since z is the code of x produced by the encoder, the decoder must decode it so that x' is similar to x.

The training of autoencoders is intended to minimize the mean squared error (MSE) between the input and the result.

MSE is the average squared difference between the output and targets. Lower values are indicative of better results. Zero means...

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