Foreword, FAQ and disclaimer

Foreword

This course concerns the latest techniques in deep learning and representation learning, focusing on supervised and unsupervised deep learning, embedding methods, metric learning, convolutional and recurrent nets, with applications to computer vision, natural language understanding, and speech recognition. The prerequisites include: DS-GA 1001 Intro to Data Science or a graduate-level machine learning course.

FAQ

Here are some answers to frequently asked questions:

• Does taking this course lead to certification?

  No, it does not. In order to offer a certification, we would have to be able to evaluate you, but the content has not been designed for this (unlike a MOOC for example). As this is a frequent request, we are thinking about proposing a certification for future editions of the course.

• How much time should I spend on this course?

  For each week, there is approximately 2h30/3h of video content. With the time dedicated to note taking and playing with the notebooks, a total estimate of 5 hours per week seems reasonable. For the rest, it depends on the level of immersion you want to achieve in a given topic (reading the referenced articles, applying what was seen in class to your own projects, etc.).

• Where to ask a question after watching a video?

  You can ask it directly in the comments section under the YouTube video in question, and Alfredo will be happy to answer it. If the question is about a specific point in the video, please include the time stamp. You can also do this on the class Discord specifically for students. It is also used to coordinate viewing groups, discuss assignments, suggest improvements, or generally discuss any topic related to the course.

• Can I use this course?

  Of course, the course is under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. This means that:

  • You may not use the material for commercial purposes.
  • You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
  • If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.

  For credit, you can use the following BibTeX:

  @misc{canziani2020nyudlsp21,  
    author = {Canziani, Alfredo & LeCun, Yann},
    title = {NYU Deep Learning, Fall 2022},
    howpublished = "\url{https://github.com/Atcold/NYU-DLFL22}",
    year = {2021},
    note = "[Online; accessed <today>]"
  }