4.6

2,501 ratings

•

636 reviews

University of Toronto

À propos de ce cours

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).

Commencez dès maintenant et apprenez aux horaires qui vous conviennent.

Recommandé : 5 hours/week

Sous-titres : English

Artificial Neural NetworkMachine LearningBayesian NetworkMathematical Optimization

Commencez dès maintenant et apprenez aux horaires qui vous conviennent.

Recommandé : 5 hours/week

Sous-titres : English

Section

Introduction to the course - machine learning and neural nets...

5 videos (Total 43 min), 8 readings, 1 quiz

What are neural networks? [8 min]8m

Some simple models of neurons [8 min]8m

A simple example of learning [6 min]5m

Three types of learning [8 min]7m

Syllabus and Course Logistics10m

Lecture Slides (and resources)10m

Setting Up Your Programming Assignment Environment10m

Installing Octave on Windows10m

Installing Octave on Mac OS X (10.10 Yosemite and 10.9 Mavericks)10m

Installing Octave on Mac OS X (10.8 Mountain Lion and Earlier)10m

Installing Octave on GNU/Linux10m

More Octave10m

Lecture 1 Quiz12m

Section

An overview of the main types of neural
network architecture ...

5 videos (Total 42 min), 1 reading, 1 quiz

Perceptrons: The first generation of neural networks [8 min]8m

A geometrical view of perceptrons [6 min]6m

Why the learning works [5 min]5m

What perceptrons can't do [15 min]14m

Lecture Slides (and resources)10m

Lecture 2 Quiz16m

Section

Learning the weights of a linear neuron ...

5 videos (Total 43 min), 2 readings, 2 quizzes

The error surface for a linear neuron [5 min]5m

Learning the weights of a logistic output neuron [4 min]3m

The backpropagation algorithm [12 min]11m

Using the derivatives computed by backpropagation [10 min]9m

Lecture Slides (and resources)10m

Forward Propagation in Neural Networks10m

Lecture 3 Quiz12m

Programming Assignment 1: The perceptron learning algorithm.12m

Section

Learning to predict the next word...

5 videos (Total 44 min), 1 reading, 1 quiz

A brief diversion into cognitive science [4 min]4m

Another diversion: The softmax output function [7 min]7m

Neuro-probabilistic language models [8 min]7m

Ways to deal with the large number of possible outputs [15 min]12m

Lecture Slides (and resources)10m

Lecture 4 Quiz14m

Section

In this module we look at why object recognition is difficult. ...

4 videos (Total 44 min), 1 reading, 2 quizzes

Achieving viewpoint invariance [6 min]5m

Convolutional nets for digit recognition [16 min]16m

Convolutional nets for object recognition [17min]17m

Lecture Slides (and resources)10m

Lecture 5 Quiz12m

Programming Assignment 2: Learning Word Representations.26m

Section

We delve into mini-batch gradient descent as well as discuss adaptive learning rates....

5 videos (Total 48 min), 1 reading, 1 quiz

A bag of tricks for mini-batch gradient descent13m

The momentum method8m

Adaptive learning rates for each connection5m

Rmsprop: Divide the gradient by a running average of its recent magnitude11m

Lecture Slides (and resources)10m

Lecture 6 Quiz10m

Section

This module explores training recurrent neural networks...

5 videos (Total 47 min), 1 reading, 1 quiz

Training RNNs with back propagation6m

A toy example of training an RNN6m

Why it is difficult to train an RNN7m

Long-term Short-term-memory9m

Lecture Slides (and resources)10m

Lecture 7 Quiz12m

Section

We continue our look at recurrent neural networks...

3 videos (Total 37 min), 1 reading, 1 quiz

Learning to predict the next character using HF [12 mins]12m

Echo State Networks [9 min]9m

Lecture Slides (and resources)10m

Lecture 8 Quiz14m

Section

We discuss strategies to make neural networks generalize better...

6 videos (Total 51 min), 1 reading, 2 quizzes

Limiting the size of the weights [6 min]6m

Using noise as a regularizer [7 min]7m

Introduction to the full Bayesian approach [12 min]10m

The Bayesian interpretation of weight decay [11 min]10m

MacKay's quick and dirty method of setting weight costs [4 min]3m

Lecture Slides (and resources)10m

Lecture 9 Quiz12m

Programming assignment 3: Optimization and generalization22m

Section

This module we look at why it helps to combine multiple neural networks to improve generalization...

5 videos (Total 49 min), 1 reading, 1 quiz

Mixtures of Experts [13 min]13m

The idea of full Bayesian learning [7 min]7m

Making full Bayesian learning practical [7 min]6m

Dropout [9 min]8m

Lecture Slides (and resources)10m

Lecture 10 Quiz12m

Section

...

5 videos (Total 56 min), 1 reading, 1 quiz

Dealing with spurious minima [11 min]11m

Hopfield nets with hidden units [10 min]9m

Using stochastic units to improv search [11 min]10m

How a Boltzmann machine models data [12 min]11m

Lecture Slides (and resources)10m

Lecture 11 Quiz10m

Section

This module deals with Boltzmann machine learning ...

5 videos (Total 53 min), 1 reading, 1 quiz

OPTIONAL VIDEO: More efficient ways to get the statistics [15 mins]14m

Restricted Boltzmann Machines [11 min]10m

An example of RBM learning [7 mins]7m

RBMs for collaborative filtering [8 mins]8m

Lecture Slides (and resources)10m

Lecture 12 Quiz14m

Section

...

3 videos (Total 36 min), 1 reading, 2 quizzes

Belief Nets [13 min]12m

The wake-sleep algorithm [13 min]13m

Lecture Slides (and resources)10m

Programming Assignment 4: Restricted Boltzmann Machines22m

Lecture 13 Quiz14m

Section

...

5 videos (Total 63 min), 1 reading, 1 quiz

Discriminative learning for DBNs [9 mins]9m

What happens during discriminative fine-tuning? [8 mins]8m

Modeling real-valued data with an RBM [10 mins]9m

OPTIONAL VIDEO: RBMs are infinite sigmoid belief nets [17 mins]17m

Lecture Slides (and resources)10m

Lecture 14 Quiz10m

Section

...

6 videos (Total 46 min), 1 reading, 2 quizzes

Deep auto encoders [4 mins]4m

Deep auto encoders for document retrieval [8 mins]8m

Semantic Hashing [9 mins]8m

Learning binary codes for image retrieval [9 mins]9m

Shallow autoencoders for pre-training [7 mins]7m

Lecture Slides (and resources)10m

Lecture 15 Quiz14m

Final Exam36m

Section

...

3 videos (Total 32 min)

OPTIONAL: Hierarchical Coordinate Frames [10 mins]9m

OPTIONAL: Bayesian optimization of hyper-parameters [13 min]13m

4.6

started a new career after completing these courses

got a tangible career benefit from this course

By NK•Jul 23rd 2017

Excellent content! Great to learn from a pioneer of the field.\n\nThe material is hard to digest, sometimes. Less text and a pointer would have helped.\n\nAnyway, great course. Thank you Prof. Hinton

By NS•Aug 13th 2017

Although It was way too tough for me, but you have to agree that you learn a lot throughout the course.\n\nI'll definitely pursue some other courses related to Deep Learning here.\n\nThanks Coursera.

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