This course contains the same content presented on Coursera beginning in 2013. It is not a continuation or update of the original course. It has been adapted for the new platform.
Please be advised that the course is suited for an intermediate level learner - comfortable with calculus and with experience programming (Python).
Introduction to the course - machine learning and neural nets
Graded: Lecture 1 Quiz
The Perceptron learning procedure
An overview of the main types of neural network architecture
Graded: Lecture 2 Quiz
The backpropagation learning proccedure
Learning the weights of a linear neuron
Graded: Lecture 3 Quiz
Graded: Programming Assignment 1: The perceptron learning algorithm.
Learning feature vectors for words
Learning to predict the next word
Graded: Lecture 4 Quiz
Object recognition with neural nets
In this module we look at why object recognition is difficult.
Graded: Lecture 5 Quiz
Graded: Programming Assignment 2: Learning Word Representations.
Optimization: How to make the learning go faster
We delve into mini-batch gradient descent as well as discuss adaptive learning rates.
Graded: Lecture 6 Quiz
Recurrent neural networks
This module explores training recurrent neural networks
Graded: Lecture 7 Quiz
More recurrent neural networks
We continue our look at recurrent neural networks
Graded: Lecture 8 Quiz
Ways to make neural networks generalize better
We discuss strategies to make neural networks generalize better
Graded: Lecture 9 Quiz
Graded: Programming assignment 3: Optimization and generalization
Combining multiple neural networks to improve generalization
This module we look at why it helps to combine multiple neural networks to improve generalization
Graded: Lecture 10 Quiz
Hopfield nets and Boltzmann machines
Graded: Lecture 11 Quiz
Restricted Boltzmann machines (RBMs)
This module deals with Boltzmann machine learning
Graded: Lecture 12 Quiz
Stacking RBMs to make Deep Belief Nets
Graded: Programming Assignment 4: Restricted Boltzmann Machines
Graded: Lecture 13 Quiz
Deep neural nets with generative pre-training
Graded: Lecture 14 Quiz
Modeling hierarchical structure with neural nets
Graded: Lecture 15 Quiz
Graded: Final Exam
Recent applications of deep neural nets