list 16 последовательности
assignment Уровень : Начальный
chat_bubble_outline Язык: английский
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Важная информация

credit_card Обучение платное
verified_user Сертификация бесплатная
timer 112 час(ы) курса

Резюме

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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Программа

  • 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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Пользователи

  • Geoffrey Hinton, Professor
    Department of Computer Science
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Разработчик

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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Платформа

Coursera

Coursera - это цифровая компания, предлагающая массовые открытые онлайн-курсы, основанные учителями компьютеров Эндрю Нгом и Стэнфордским университетом Дафни Коллер, расположенные в Маунтин-Вью, штат Калифорния.

Coursera работает с ведущими университетами и организациями, чтобы сделать некоторые из своих курсов доступными в Интернете, и предлагает курсы по многим предметам, включая: физику, инженерию, гуманитарные науки, медицину, биологию, социальные науки, математику, бизнес, информатику, цифровой маркетинг, науку о данных и другие предметы.

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