Dry goods collection! 639 page “deep learning: deep learning” course ppt

Time:2021-2-20

It’s not easy to get a PhD. Recently, my tutor sent me a deep learning course ppt. It’s a very hard core learning course with excellent pictures and text.As a note, I also pay the download link for you to study together

The latest development of deep machine learning makes great progress in vision recognition, speech and text understanding or autonomous agent system. In this context, this course will explore the details of deep learning architecture, focusing on learning the end-to-end model of these tasks. Students will learn to implement, train and debug their own neural networks, and have a detailed understanding of the frontier research in this field. The course will also introduce the latest innovations of reasoning methods, including differential reasoning, confrontational training and Bayesian deep learning.

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Ppt link:https://pan.baidu.com/s/1nugql5kVBP38kaCjhvnRCAExtraction code: wmgn

Course introduction

The latest development of deep machine learning makes great progress in vision recognition, speech and text understanding or autonomous agent system. In this context, this course will explore the details of deep learning architecture, focusing on learning the end-to-end model of these tasks. Students will learn to implement, train and debug their own neural networks, and have a detailed understanding of the frontier research in this field. The course will also introduce the latest innovations of reasoning methods, including differential reasoning, confrontational training and Bayesian deep learning.

Syllabus

  • Fundamentals of machine learning
  • neural network
  • Convolutional neural network
  • Training neural network
  • Recurrent neural network
  • Automatic encoder and generation model
  • Generative countermeasure network
  • Uncertainty
  • Antagonistic attack and defense
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    About Instructor
    Gilles Louppe is an associate professor of artificial intelligence and deep learning at the University of Liege in Belgium. He was a postdoctoral assistant in the Department of physics and the center for data science at New York University and was closely associated with the atlas experiment at CERN. His research is at the intersection of machine learning, artificial intelligence and physical science. His current research interests include using and designing new machine learning algorithms to deal with data-driven problems from basic science in a new and revolutionary way.
    Personal website:http://www.montefiore.ulg.ac.be/~glouppe
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Ppt link:https://pan.baidu.com/s/1nugql5kVBP38kaCjhvnRCAExtraction code: wmgn

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