Computer Science Dept., Ferdowsi University of Mashhad

Neural Networks

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.

• Course Jupyter Book

2024 Instructor: Mahmood Amintoosi

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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.

Optional Reference/Learning Materials

Deep learning resources

• 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

ML-related textbooks

• 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.

Math for ML

• Mathematics for Machine Learning
• The Matrix Calculus You Need For Deep Learning
• Introduction to Optimizers

Other ML resources

• A Course in Machine Learning
• Nando de Freitas lecture videos and online course

Interesting ML Competition Write-Ups

• Diabetic retinopathy Kaggle competition write-up
• Galaxy Zoo Kaggle competition write-up
• National Data Science Bowl competition write-up

Build

• jupyter-book build ./
• ghp-import -n -p -f ./_build/html
• jupyter-book build --builder pdflatex ./