Wolfram U

The Neural Net Framework: A Gentle Introduction

Estimated Time: 1 h 45 min

Course Level: Beginner


Neural networks are becoming better and increasingly more popular for machine learning and artificial intelligence applications due to more data and better technology being available. Learn how neural networks can be viewed as differentiable programs in the context of calculus. The course covers fundamental concepts related to building, training and validating neural net models. Examples will include both linear and nonlinear regression models as well as classification tasks, specifically image classification. See how the symbolic nature of Wolfram Language and the Neural Net Framework itself make it easy to take apart layers in the network and create visualizations to help you understand how it works.

Featured Products & Technologies: Wolfram Language (available in Mathematica and Wolfram|One), Wolfram Neural Net Framework

You'll Learn To

  • Identify the advancements that have made neural networks better and more popular in recent years
  • View neural networks as differentiable programs in the context of mathematical operations from calculus
  • Build simple net models for regression and classification
  • Train different models with the help of available data
  • Fine-tune and validate the performance of models with the help of additional data
  • Explore the Neural Net Repository