Resources
Textbooks
All of these are available digitally from the NYU library:
- Data Science and Analytics with Python (DSAP) by Jesús Rogel-Salazar
- Deep Learning with PyTorch (DLPT) by Thomas Viehmann, Eli Stevens, Luca Pietro Giovanni Antiga
- Think Python (TP) by Allen B. Downey
- Hands-On Data Analysis with Pandas (HODAP) by Stefanie Molin
- Machine Learning for Audio, Image and Video Analysis: Theory and Applications (MLAIVA) by Francesco Camastra, Alessandro Vinciarelli
- Deep Learning for Vision Systems (DLVS) by Mohamed Elgendy
- Deep Learning (DL) by Ian Goodfellow, Yoshua Bengio, Aaron Courville
- Dive into Deep Learning (D2L) by Aston Zhang, Zachary C. Lipton, Mu Li, Alexander J. Smola
- Fundamentals of Music Processing: Using Python and Jupyter Notebooks (FMP) by Meinard Müller
- Music Similarity and Retrieval: An Introduction to Audio- and Web-Based Strategies (MSR) by Peter Knees, Markus Schedl
- Hands-On Image Processing with Python (HOIPP) by Sandipan Dey
- Introduction to Machine Learning (IML) by Ethem Alpaydin
- Machine Learning: The Art and Science of Algorithms that Make Sense of Data (MLASA) by Peter A. Flach
- Introduction to Machine Learning with Python (IMLP) by Andreas C. Müller, Sarah Guido
- Natural Language Processing with Transformers (NLPT) by Lewis Tunstall, Leandro von Werra, Thomas Wolf
- Hands-On Generative AI with Transformers and Diffusion Models (Omar Sanseviero, Pedro Cuenca, Apolinário Passos, Jonathan Whitaker) by HOGAI
Other Technical Resources
Github
- GitHub’s GitHub Tutorial
- Interactive Git Cheatsheet
Python
- Stanford’s CS231n Python Tutorial
- DEV.io’s Python for JS developers
- Another Python for JS developers