Artificial Neural Networks View more

A quick dive into a cutting-edge computational method for learning.

Book 28 Lessons

Course description

This course was written in collaboration with machine learning researchers and lecturers from MIT, Princeton, and Stanford.

This interactive course dives into the fundamentals of artificial neural networks, from the basic frameworks to more modern techniques like adversarial models.

Topics covered

  • Adversarial Networks
  • Backpropagation
  • Convolutional Networks
  • Gradient Descent
  • Linear Classifiers
  • LSTM
  • Optimization
  • Perceptron
  • Recurrent Networks
  • Reinforcement Learning
  • Stochastic Networks
  • Variational Autoencoders

Prerequisites and next steps

You'll need mastery of algebra. A basic understanding of calculus and probability will be helpful.