list 16 séquences
assignment Niveau : Introductif
chat_bubble_outline Langue : Anglais
card_giftcard 896 points
Avis de la communauté

Les infos clés

credit_card Formation gratuite
verified_user Certification gratuite
timer 112 heures de cours

En résumé

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).

more_horiz Lire plus
more_horiz Lire moins

Le programme

  • 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

Les intervenants

  • Geoffrey Hinton, Professor
    Department of Computer Science

Le concepteur

University of Toronto
Established in 1827, the University of Toronto has one of the strongest research and teaching faculties in North America, presenting top students at all levels with an intellectual environment unmatched in depth and breadth on any other Canadian campus.

La plateforme


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.

Vous êtes le concepteur de ce MOOC ?
Quelle note donnez-vous à cette ressource ?