Learn about artificial neural networks and how they're being used for machine learning, as applied to speech and object recognition, image segmentation, modeling language and human motion, etc. We'll emphasize both the basic algorithms and the practical tricks needed to get them to work well.
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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).
Syllabus
WEEK 1
Introduction
Introduction to the course - machine learning and neural nets
Graded: Lecture 1 Quiz
WEEK 2
The Perceptron learning procedure
An overview of the main types of neural network architecture
Graded: Lecture 2 Quiz
WEEK 3
The backpropagation learning proccedure
Learning the weights of a linear neuron
Graded: Lecture 3 Quiz
Graded: Programming Assignment 1: The perceptron learning algorithm.
WEEK 4
Learning feature vectors for words
Learning to predict the next word
Graded: Lecture 4 Quiz
WEEK 5
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.
WEEK 6
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
WEEK 7
Recurrent neural networks
This module explores training recurrent neural networks
Graded: Lecture 7 Quiz
WEEK 8
More recurrent neural networks
We continue our look at recurrent neural networks
Graded: Lecture 8 Quiz
WEEK 9
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
WEEK 10
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
WEEK 11
Hopfield nets and Boltzmann machines
Graded: Lecture 11 Quiz
WEEK 12
Restricted Boltzmann machines (RBMs)
This module deals with Boltzmann machine learning
Graded: Lecture 12 Quiz
WEEK 13
Stacking RBMs to make Deep Belief Nets
Graded: Programming Assignment 4: Restricted Boltzmann Machines
Graded: Lecture 13 Quiz
WEEK 14
Deep neural nets with generative pre-training
Graded: Lecture 14 Quiz
WEEK 15
Modeling hierarchical structure with neural nets
Graded: Lecture 15 Quiz
Graded: Final Exam
WEEK 16
Recent applications of deep neural nets
