You're reading from Hands-On Reinforcement Learning with Python

Product typeBook

Published inJun 2018

Reading LevelIntermediate

PublisherPackt

ISBN-139781788836524

Edition1st Edition

Languages

Python

Tools

TensorFlow Keras

Concepts

Reinforcement Learning

Author (1)

Sudharsan Ravichandiran

Deep diving into ANN

We know that in artificial neurons, we multiply the input by weights, add bias to them and apply an activation function to produce the output. Now, we will see how this happens in a neural network setting where neurons are arranged in layers. The number of layers in a network is equal to the number of hidden layers plus the number of output layers. We don't take the input layer into account. Consider a two-layer neural network with one input layer, one hidden layer, and one output layer, as shown in the following diagram:

Let's say we have two inputs, x₁ and x₂, and we have to predict the output y. Since we have two inputs, the number of neurons in the input layer will be two. Now, these inputs will be multiplied by weights and then we add bias and propagate the resultant value to the hidden layer where the activation function will be applied. So...

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Hands-On Reinforcement Learning with Python

Published in: Jun 2018Publisher: PacktISBN-13: 9781788836524

Author (1)

Sudharsan Ravichandiran

Sudharsan Ravichandiran is a data scientist and artificial intelligence enthusiast. He holds a Bachelors in Information Technology from Anna University. His area of research focuses on practical implementations of deep learning and reinforcement learning including natural language processing and computer vision. He is an open-source contributor and loves answering questions on Stack Overflow.
Read more about Sudharsan Ravichandiran

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Author (1)

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