This course provides an introduction to the state-of-the-art Neural Net Framework in Wolfram Language. You will learn how to explore the Wolfram Neural Net Repository for pre-built and pre-trained models and how to apply them to your own dataset. Also see how you can use transfer learning to adapt models for your own applications. The course will then discuss the building blocks of neural nets, along with instructions for putting them together within a symbolic framework to build your own neural network. Simple examples of training and testing a neural network will be discussed, along with options to inspect output from hidden layers of the net. Earn a certificate of course completion by attending this online class and passing the quiz.

Learn more about this course by going to the Wolfram U catalog.
  • What Is a Neural Network?
  • The Neural Net Repository
  • Building Blocks of the Neural Net Framework
  • Training, Testing and Looking inside a Neural Net
Abrita Chakravarty
Wolfram Certified Instructor
Mike Yeh
Wolfram Technology Engineer
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