Artificial Neural Networks
A quick dive into a cutting-edge computational method for learning.
Learning Problems for Neural Networks
Computationally Modeling The Brain
Computational Models of The Neuron
Vectors for Neural Networks
Matrices for Neural Networks
Optimization for Neural Networks
Perceptrons as Linear Classifiers
Perceptron Learning Algorithm
Dealing with Perceptron Limitations
Basics and Motivation
Practical Example
Multilayer Perceptron - Model Complexity
Gradient Descent
Backpropagation - Updating Parameters
Backpropagation
Vanishing and Exploding Gradient
Convolutional Neural Networks - Overview
Convolutions and Striding
Convolutional Neural Networks - Pooling
Applications and Performance
Recurrent Neural Networks
Training Recurrent Neural Networks
Long Short-Term Memory
Stochastic Neural Networks
Generative Adversarial Networks
Variational Autoencoders
Word2Vec
Reinforcement Learning
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. You’ll answer questions such as how a computer can distinguish between pictures of dogs and cats, and how it can learn to play great chess. Using inspiration from the human brain and some linear algebra, you’ll gain an intuition for why these models work – not just a collection of formulas. This course is ideal for students and professionals seeking a fundamental understanding of neural networks, or brushing up on basics.
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.