深度学习要领

Deep Learning Essentials

Do you want to learn how machines can learn tasks we thought only human brains could perform? Then take this Deep Learning course developed by IVADO, Mila and Université de Montréal: an extensive overview of the essentials of deep learning, this ground-breaking technology already prevalent in our lives and spanning all sectors.

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蒙特利尔大学
edX
  • 完成时间大约为 5
  • 中级
  • 英语
注:因开课平台的各种因素变化,以上开课日期仅供参考

你将学到什么

Understand the basics and terminology related to Deep Learning

Identify the types of neural networks to use to solve different types of problems

Get familiar with Deep Learning libraries through practical and tutorial sessions

课程概况

Gain a good understanding of what Deep Learning is, what types of problems it resolves, and what are the fundamental concepts and methods it entails. The course developed by IVADO, Mila and Université de Montréal offers diversified learning tools for you to fully grasp the extent of this ground-breaking cross-cutting technology, a critical need in the field.

IVADO, a scientific and economic data science hub bridging industrial, academic and government partners with expertise in digital intelligence designed the course, and the world-renowned Mila, rallying researchers specialized in Deep Learning, created the content. Mila’s founder and IVADO’s scientific director, Yoshua Bengio, also a professor at Université de Montréal, is a world-leading expert in artificial intelligence and a pioneer in deep learning as well as the scientific director of this course. He is also a joint recipient of the 2018 A.M. Turing Award, “the Nobel Prize of Computing”, for conceptual and engineering breakthroughs that have made deep neural networks a critical component of computing.

Deep Learning is an extension of Machine Learning where machines can learn by experience without human intervention. It is largely influenced by the human brain in the fact that algorithms, or artificial neural networks, are able to learn from massive amounts of data and acquire skills that a human brain would. Thus, Deep learning is now able to tackle a large variety of tasks that were considered out of reach a few years ago in computer vision, signal processing, natural language processing, robotics, and sequential decision-making. Because of these recent advances, various industries are now deploying deep learning models that impact various economic sectors such as transport, health, finance, energy, as well as our daily life in general.

If you are a professional, a scientist or an academic with basic knowledge in mathematics and programming, this MOOC is designed for you! Atop the rich Deep Learning content, discover issues of bias and discrimination in machine learning and benefit from this sociotechnical topic that has proven to be a great eye-opener for many.

课程大纲

MODULE 1 Machine Learning (ML) and experimental protocol

Introduction to ML
ML Tools

MODULE 2 Introduction to Deep Learning

Modular approaches
Backpropagation
Optimization

MODULE 3 Intro to Convolutional Neural Networks (CNN)

Introduction to CNN
CNN architectures

MODULE 4 Introduction to Recurrent Neural Networks

Sequence to sequence models
Concepts in natural language processing

MODULE 5 Bias and discrimination in ML

Differences of fairness
Fairness in pre- in- and post-processing

预备知识

A minimal knowledge of programming (ideally Python) and basic knowledge in mathematics (linear algebra, statistics) is desirable.

常见问题

What is the complete list of speakers for this course?

Mirko BRONZI

Golnoosh FARNADI

Gaétan MARCEAU-CARON

Jeremy PINTO

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