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assignment 等级:入门
chat_bubble_outline 语言 : 英语
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关键信息

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timer 总共112个小时

关于内容

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).

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课程大纲

  • 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
     
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教师

  • Geoffrey Hinton, Professor
    Department of Computer Science
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内容设计师

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.
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平台

Coursera

Coursera是一家数字公司,提供由位于加利福尼亚州山景城的计算机教师Andrew Ng和达芙妮科勒斯坦福大学创建的大型开放式在线课程。

Coursera与顶尖大学和组织合作,在线提供一些课程,并提供许多科目的课程,包括:物理,工程,人文,医学,生物学,社会科学,数学,商业,计算机科学,数字营销,数据科学 和其他科目。

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