An introduction to Neural Networks with Python and Pytorch which covers optmization, neural network basics, convolutional neural networks, and advanced topics such as autoencoders and generative adversarial networks.
2024 Instructor: Mahmood Amintoosi
Find the lecture schedule below. I'm developing new material for this course, but I've included links to Tomas Beuzen lectures and other useful videos for those that are interested.
| # | Topic | Optional Watching/Reading |
|---|---|---|
| 1 | Floating Point Errors |
|
| 2 | Optimization and Gradient Descent | |
| 3 | Stochastic Gradient Descent | |
| 4 | Introduction to Neural Networks & PyTorch | |
| 5 | Training Neural Networks | |
| 6 | Convolutional Neural Networks Part 1 | |
| 7 | Convolutional Neural Networks Part 2 | TBD |
| 8 | Advanced Neural Networks | TBD |
You are responsible for the following deliverables, which will determine your course grade:
| Assessment | Weight |
|---|---|
| Lab Assignment 1 | 15% |
| Lab Assignment 2 | 15% |
| Quiz 1 | 20% |
| Lab Assignment 3 | 15% |
| Lab Assignment 4 | 15% |
| Quiz 2 | 20% |
Labs are Jupyter notebooks comprised of more comprehensive exercises aimed at demonstrating and reinforcing concepts learned during lectures. Quizzes will be conducted on Canvas in week 3 and week 5, are open book and are typically 40 mins long with a focus on short-answer questions. More information on quizzes will be provided closer to their dates.
- Dive into Deep Learning, a book based on STAT 157 at UC Berkeley.
- Deep learning YouTube series by 3Blue1Brown.
- Neural Networks and Deep Learning (free online book).
- Deep Learning. Ian Goodfellow, Yoshua Bengio and Aaron Courville.
- Deep Learning with Python. Jason Brownlee.
- Stanford UFLDL tutorial (or here)
- Geoff Hinton Coursera lectures
- CS231n: Convolutional Neural Networks for Visual Recognition (Stanford)
- Grokking Deep Learning
- Practical Deep Learning For Coders, Part 1 and some more resources on their blog here
- A Guide to Deep Learning
- Awesome Deep Learning, which is a list of other resources
- Full Stack Deep Learning
- Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems by Aurélien Géron. Code/notebooks available here. (Endorsed by an MDS student!)
- James, Gareth; Witten, Daniela; Hastie, Trevor; and Tibshirani, Robert. An Introduction to Statistical Learning: with Applications in R. 2014. Plus Python code and more Python code.
- Russell, Stuart, and Peter Norvig. Artificial intelligence: a modern approach. 1995.
- David Poole and Alan Mackwordth. Artificial Intelligence: foundations of computational agents. 2nd edition (2017). Free e-book.
- Kevin Murphy. Machine Learning: A Probabilistic Perspective. 2012.
- Christopher Bishop. Pattern Recognition and Machine Learning. 2007.
- Pang-Ning Tan, Michael Steinbach, Vipin Kumar. Introduction to Data Mining. 2005.
- Mining of Massive Datasets. Jure Leskovec, Anand Rajaraman, Jeffrey David Ullman. 2nd ed, 2014.
- Mathematics for Machine Learning
- The Matrix Calculus You Need For Deep Learning
- Introduction to Optimizers
- Diabetic retinopathy Kaggle competition write-up
- Galaxy Zoo Kaggle competition write-up
- National Data Science Bowl competition write-up
- jupyter-book build ./
- ghp-import -n -p -f ./_build/html
- jupyter-book build --builder pdflatex ./