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Reinforcement Learning with TensorFlow
book

Reinforcement Learning with TensorFlow

by Sayon Dutta
April 2018
Intermediate to advanced content levelIntermediate to advanced
334 pages
10h 18m
English
Packt Publishing
Content preview from Reinforcement Learning with TensorFlow

The computational graph

A basic neural network consists of forward propagation followed by a backward propagation. As a result, it consists of a series of steps that includes the values of different nodes, weights, and biases, as well as derivatives of cost function with regards to all the weights and biases. In order to keep track of these processes, the computational graph comes into the picture. The computational graph also keeps track of chain rule differentiation irrespective of the depth of the neural network.

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Publisher Resources

ISBN: 9781788835725Supplemental Content