Les infos clés
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. 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).
- Week 1 - Introduction
Introduction to the course - machine learning and neural nets
- Week 2 - The Perceptron learning procedure
An overview of the main types of neural network architecture
- Week 3 - The backpropagation learning proccedure
Learning the weights of a linear neuron
- Week 4 - Learning feature vectors for words
Learning to predict the next word
- Week 5 - Object recognition with neural nets
In this module we look at why object recognition is difficult.
- Week 6 - Optimization: How to make the learning go faster
We delve into mini-batch gradient descent as well as discuss adaptive learning rates.
- Week 7 - Recurrent neural networks
This module explores training recurrent neural networks
- Week 8 - More recurrent neural networks
We continue our look at recurrent neural networks
- Week 9 - Ways to make neural networks generalize better
We discuss strategies to make neural networks generalize better
- 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
- Week 11 - Hopfield nets and Boltzmann machines
- Week 12 - Restricted Boltzmann machines (RBMs)
This module deals with Boltzmann machine learning
- Week 13 - Stacking RBMs to make Deep Belief Nets
- Week 14 - Deep neural nets with generative pre-training
- Week 15 - Modeling hierarchical structure with neural nets
- Week 16 - Recent applications of deep neural nets
- Geoffrey Hinton, Professor
Department of Computer Science
Coursera est une entreprise numérique proposant des formations en ligne ouverte à tous fondée par les professeurs d'informatique Andrew Ng et Daphne Koller de l'université Stanford, située à Mountain View, Californie.
Ce qui la différencie le plus des autres plateformes MOOC, c'est qu'elle travaille qu'avec les meilleures universités et organisations mondiales et diffuse leurs contenus sur le web.