Deep Learning Using PyTorch
In this course, you will learn the foundations of Deep Learning, understand how to build
neural networks, and learn how to lead successful machine learning projects. You will learn
about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, and more.
Slides and Papers:
Recommended Slides & Papers:
- Video of lecture by Ian Goodfellow and discussion of Chapter 1 at a reading group in San Francisco organized by Alena Kruchkova
- Paper: On the Origin of Deep Learning by Haohan Wang and Bhiksha Raj
Applied Mathematics and Machine Learning Basics:
- Blog: Learning PyTorch with Exampls by Justin Johnson.
This tutorial introduces the fundamental concepts of PyTorch through self-contained examples.
Building Dynamic Models Using the Subclassing API:
Deep Feedforward Networks
Regularization for Deep Learning
Optimization for Training Deep Models
- NoteBook: Chapter 8: Using Convolutions to Generalize from Deep Learning with PyTorch by Eli Stevens and Luca Antiga
- NoteBook: [Chapter 3: Convolutional Neural Networks](https://github.com/falloutdurham/beginners-pytorch-deep-learning/tree/master/chapter3](https://www.oreilly.com/library/view/programming-pytorch-for/9781492045342/) by Ian Pointer
Sequence Modeling: Recurrent and Recursive Networks
- Deep Learning via Pytorch by Ayoosh Kathuria
Optuna is an automatic hyperparameter optimization software framework, particularly designed for machine learning.
Generative Adversarial Networks
Slide: Generative Adversarial Networks (GANs) by Binglin, Shashank, and Bhargav
Paper: NIPS 2016 Tutorial: Generative Adversarial Networks by Ian Goodfellow
Graph Neural Networks:
Class Time and Location:
Saturday and Monday 10:30-12:00 AM (Fall 2020)
Recitation and Assignments:
Tuesday 16:00-18:00 PM (Fall 2020),
Refer to the following link to check the assignments.
Projects are programming assignments that cover the topic of this course. Any project is written by Jupyter Notebook. Projects will require the use of Python 3.7, as well as additional Python libraries.
Google Colab is a free cloud service and it supports free GPU!
Fascinating Guides For Machine Learning:
The students can include mathematical notation within markdown cells using LaTeX in their Jupyter Notebooks.
- A Brief Introduction to LaTeX PDF
- Math in LaTeX PDF
- Sample Document PDF
- TikZ: A collection Latex files of PGF/TikZ figures (including various neural networks) by Petar Veličković.
- Projects and Midterm – 50%
- Endterm – 50%
- Midterm Examination: Saturday 1399/09/01, 10:30-12:00
- Final Examination: Wednesday 1399/10/24, 14:00-16:00
General mathematical sophistication; and a solid understanding of Algorithms, Linear Algebra, and
Probability Theory, at the advanced undergraduate or beginning graduate level, or equivalent.
Probability and Statistics:
Have a look at some reports of Kaggle or Stanford students (CS224N, CS224D) to get some general inspiration.
It is necessary to have a GitHub account to share your projects. It offers
plans for both private repositories and free accounts. Github is like the hammer in your toolbox,
therefore, you need to have it!
Academic Honor Code:
Honesty and integrity are vital elements of the academic works. All your submitted assignments must be entirely your own (or your own group’s).
We will follow the standard of Department of Mathematical Sciences approach:
- You can get help, but you MUST acknowledge the help on the work you hand in
- Failure to acknowledge your sources is a violation of the Honor Code
- You can talk to others about the algorithm(s) to be used to solve a homework problem; as long as you then mention their name(s) on the work you submit
- You should not use code of others or be looking at code of others when you write your own: You can talk to people but have to write your own solution/code
I will be having office hours for this course on Saturday (09:00 AM–10:00 AM). If this is not convenient, email me at email@example.com or talk to me after class.