Об этом курсе
4.6
2,561 ratings
646 reviews
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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Предполагаемая нагрузка: 5 hours/week

Прибл. 45 ч. на завершение
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English

Субтитры: English

Приобретаемые навыки

Artificial Neural NetworkRestricted Boltzmann MachineDeep LearningRecurrent Neural Network
Globe

Только онлайн-курсы

Начните сейчас и учитесь по собственному графику.
Calendar

Гибкие сроки

Назначьте сроки сдачи в соответствии со своим графиком.
Clock

Предполагаемая нагрузка: 5 hours/week

Прибл. 45 ч. на завершение
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English

Субтитры: English

Программа курса: что вы изучите

1

Раздел
Clock
2 ч. на завершение

Introduction

Introduction to the course - machine learning and neural nets...
Reading
5 видео (всего 43 мин.), 8 материалов для самостоятельного изучения, 1 тест
Video5 видео
What are neural networks? [8 min]8мин
Some simple models of neurons [8 min]8мин
A simple example of learning [6 min]5мин
Three types of learning [8 min]7мин
Reading8 материала для самостоятельного изучения
Syllabus and Course Logistics10мин
Lecture Slides (and resources)10мин
Setting Up Your Programming Assignment Environment10мин
Installing Octave on Windows10мин
Installing Octave on Mac OS X (10.10 Yosemite and 10.9 Mavericks)10мин
Installing Octave on Mac OS X (10.8 Mountain Lion and Earlier)10мин
Installing Octave on GNU/Linux10мин
More Octave10мин
Quiz1 практическое упражнение
Lecture 1 Quiz12мин

2

Раздел
Clock
1 ч. на завершение

The Perceptron learning procedure

An overview of the main types of neural network architecture ...
Reading
5 видео (всего 42 мин.), 1 материал для самостоятельного изучения, 1 тест
Video5 видео
Perceptrons: The first generation of neural networks [8 min]8мин
A geometrical view of perceptrons [6 min]6мин
Why the learning works [5 min]5мин
What perceptrons can't do [15 min]14мин
Reading1 материал для самостоятельного изучения
Lecture Slides (and resources)10мин
Quiz1 практическое упражнение
Lecture 2 Quiz16мин

3

Раздел
Clock
1 ч. на завершение

The backpropagation learning proccedure

Learning the weights of a linear neuron ...
Reading
5 видео (всего 43 мин.), 2 материалов для самостоятельного изучения, 2 тестов
Video5 видео
The error surface for a linear neuron [5 min]5мин
Learning the weights of a logistic output neuron [4 min]3мин
The backpropagation algorithm [12 min]11мин
Using the derivatives computed by backpropagation [10 min]9мин
Reading2 материала для самостоятельного изучения
Lecture Slides (and resources)10мин
Forward Propagation in Neural Networks10мин
Quiz2 практического упражнения
Lecture 3 Quiz12мин
Programming Assignment 1: The perceptron learning algorithm.12мин

4

Раздел
Clock
1 ч. на завершение

Learning feature vectors for words

Learning to predict the next word...
Reading
5 видео (всего 44 мин.), 1 материал для самостоятельного изучения, 1 тест
Video5 видео
A brief diversion into cognitive science [4 min]4мин
Another diversion: The softmax output function [7 min]7мин
Neuro-probabilistic language models [8 min]7мин
Ways to deal with the large number of possible outputs [15 min]12мин
Reading1 материал для самостоятельного изучения
Lecture Slides (and resources)10мин
Quiz1 практическое упражнение
Lecture 4 Quiz14мин
4.6
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28%

начал новую карьеру, пройдя эти курсы
Briefcase

83%

получил значимые преимущества в карьере благодаря этому курсу

Лучшие рецензии

автор: NSAug 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.

автор: NRDec 2nd 2017

I would like to thank you all for this great course. To Prof Hinton, especially, it's amazing how much value is in this course and to make it available for entire world is just great. Thanks again !

Преподаватель

Geoffrey Hinton

Professor
Department of Computer Science

О 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. ...

Часто задаваемые вопросы

  • Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.

  • When you purchase a Certificate you get access to all course materials, including graded assignments. Upon completing the course, your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

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