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

28 Lessons

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.

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