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You're reading from Statistics for Machine Learning

Product typeBook

Published inJul 2017

Reading LevelIntermediate

PublisherPackt

ISBN-139781788295758

Edition1st Edition

Languages

Python

Concepts

Statistics

Author (1)

Pratap Dangeti

Forward propagation and backpropagation

Forward propagation and backpropagation are illustrated with the two hidden layer deep neural networks in the following example, in which both layers get three neurons each, in addition to input and output layers. The number of neurons in the input layer is based on the number of x (independent) variables, whereas the number of neurons in the output layer is decided by the number of classes the model needs to be predicted.

For ease, we have shown only one neuron in each layer; however, the reader can attempt to create other neurons within the same layer. Weights and biases are initiated from some random numbers, so that in both forward and backward passes, these can be updated in order to minimize the errors altogether.

During forward propagation, features are input to the network and fed through the following layers to produce the output activation. If we see in the hidden layer 1, the activation obtained is the combination of bias weight 1 and weighted...

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Statistics for Machine Learning

Published in: Jul 2017Publisher: PacktISBN-13: 9781788295758

Author (1)

Pratap Dangeti

Pratap Dangeti develops machine learning and deep learning solutions for structured, image, and text data at TCS, analytics and insights, innovation lab in Bangalore. He has acquired a lot of experience in both analytics and data science. He received his master's degree from IIT Bombay in its industrial engineering and operations research program. He is an artificial intelligence enthusiast. When not working, he likes to read about next-gen technologies and innovative methodologies.
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Forward propagation and backpropagation

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