list 16 sequences
assignment Level : Introductive
chat_bubble_outline Language : English
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timer 112 hours in total

About the content

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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  • 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

Content Designer

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.



Coursera is a digital company offering massive open online course founded by computer teachers Andrew Ng and Daphne Koller Stanford University, located in Mountain View, California. 

Coursera works with top universities and organizations to make some of their courses available online, and offers courses in many subjects, including: physics, engineering, humanities, medicine, biology, social sciences, mathematics, business, computer science, digital marketing, data science, and other subjects.

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