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Deep Learning with TensorFlow

Deep Learning with TensorFlow

By : Zaccone, Karim
3 (4)
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Deep Learning with TensorFlow

Deep Learning with TensorFlow

3 (4)
By: Zaccone, Karim

Overview of this book

Deep learning is a branch of machine learning algorithms based on learning multiple levels of abstraction. Neural networks, which are at the core of deep learning, are being used in predictive analytics, computer vision, natural language processing, time series forecasting, and to perform a myriad of other complex tasks. This book is conceived for developers, data analysts, machine learning practitioners and deep learning enthusiasts who want to build powerful, robust, and accurate predictive models with the power of TensorFlow, combined with other open source Python libraries. Throughout the book, you’ll learn how to develop deep learning applications for machine learning systems using Feedforward Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Autoencoders, and Factorization Machines. Discover how to attain deep learning programming on GPU in a distributed way. You'll come away with an in-depth knowledge of machine learning techniques and the skills to apply them to real-world projects.
Table of Contents (13 chapters)
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12
Index

The Q-Learning algorithm


Solving an RL problem requires an estimate, during the learning process, of an evaluation function. This function must be able to assess, through the sum of the rewards, the success of a policy.

The basic idea of Q-Learning is that the algorithm learns the optimal evaluation function for the entire space of states and actions (S × A). This so-called Q-function provides a match in the form Q: S × A -> R, where R is the expected value of the future rewards of an action executed in the state, . Once the agent has learned the optimal function, Q, it will be able to recognize what action will lead to the highest future reward in a certain state.

One of the most commonly used examples of implementing the Q-Learning algorithm involves the use of a table. Each cell of the table is a value Q(s; a)= R and it is initialized to 0. The action , performed by the agent, is chosen using a policy which is epsilon-greedy with respect to Q.

The basic idea of the Q-Learning algorithm...

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