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
I should mention that the original material was from Tomas Beuzen's course github. I have modified his contents to suit my own needs and preferences. I would like to thank him for his great work and generosity.
- 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
In notebooks folder:
- jupyter-book build ./
- copy ../require.js ./_build
- ghp-import -n -p -f ./_build/html
- jupyter-book build --builder pdflatex ./