Products Machine Learning Crash Course Crash Course Course Introduction to Neural Networks Neural networks are a more sophisticated version of feature crosses. In essence, neural networks learn the appropriate feature crosses for you. Intro to Neural Networks A Linear Model Three blue circles in a row connected by arrows to a green circle above them image/svg+xml Input Output Add Complexity: Non-Linear? Three blue circles in a row labeled "Input" connected by arrows to a row of yellow circles labeled "Hidden Layer" above them, which are in turn connected to a green circle labeled "Output" at the top. image/svg+xml Output Hidden Layer Input More Complex: Non-Linear? image/svg+xml Output Hidden Layer 2 Hidden Layer 1 Input Adding a Non-Linearity The same as the previous figure, except that a row of pink circles labeled 'Non-Linear Transformation Layer' has been added in between the two hidden layers. image/svg+xml Output Hidden Layer 2 Non-Linear Transformation Layer (a.k.a. Activation Function) Hidden Layer 1 Input We Usually Don't Draw Non-Linear Transforms Our Favorite Non-Linearity A graph with slope of 0 and then linear once it passes x=0 image/svg+xml Relu Rectified Linear Unit F(x)=max(0,x) Neural Nets Can Be Arbitrarily Complex A complex neural network image/svg+xml Hidden2 Hidden1 Input Output Help Center Previous arrow_back Check Your Understanding Next arrow_forward Anatomy Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 3.0 License, and code samples are licensed under the Apache 2.0 License. For details, see our Site Policies. Java is a registered trademark of Oracle and/or its affiliates. Last updated August 22, 2018.