From the course: Deep Learning: Getting Started
Join today to access over 24,100 courses taught by industry experts.
Activation functions
From the course: Deep Learning: Getting Started
Activation functions
- [Instructor] An Activation function plays an important role in creating the output of a node in the neural network. An Activation function, takes in the matrix output of the node and determines if and how the node will propagate its information to the next layer. Activation functions act as filters to reduce noise and also normalize the output, which can get fairly large due to matrix multiplications. It converts the output to a nonlinear value. They serve as a critical step in helping a neural network learn specific patterns in data. Here is the list of some of the most popular activation functions. Each of these functions have specific advantages, shortcomings and applications. They can take in an output matrix and deliver another output matrix of the same dimension. A sigmoid function delivers an output in the range of zero to one, based on the input values. When it has a value of zero, it means that it does not pass its learnings to the next layer. A Tanh function normalizes the…
Practice while you learn with exercise files
Download the files the instructor uses to teach the course. Follow along and learn by watching, listening and practicing.
Download courses and learn on the go
Watch courses on your mobile device without an internet connection. Download courses using your iOS or Android LinkedIn Learning app.