Recommended reading

Some potentially useful references for the course are listed below. None are required. I may add more as we go.

General machine learning

Howard Huang et al., Deep Learning with PyTorch, 2nd ed.
Aston Zhang et al., Dive into Deep Learning
François Chollet and Matthew Watson, Deep Learning with Python, 3rd ed.

Computer vision

Antonio Torralba, Phillip Isola and William T. Freeman, Foundations of Computer Vision
Richard Szeliski, Computer Vision: Algorithms and Applications, 2nd ed.

Text and language models

Sebastian Raschka, Build a Large Language Model (From Scratch).
Daniel Jurafsky and James H. Martin, Speech and Language Processing, 3rd-edition draft.
Jay Alammar and Maarten Grootendorst, Hands-On Large Language Models (2024).
Chip Huyen, AI Engineering: Building Applications with Foundation Models.

Tracking

Roger R. Labbe Jr., Kalman and Bayesian Filters in Python.
Alex Bewley et al., Simple online and realtime tracking (SORT).

For optional background on tracking benchmarks, see MOTChallenge.

# Recommended reading {.unnumbered}

Some potentially useful references for the course are listed below. None are required. I may add more as we go.

## General machine learning

- Howard Huang et al., [*Deep Learning with PyTorch*, 2nd ed.](https://www.manning.com/books/deep-learning-with-pytorch-second-edition)
- Aston Zhang et al., [*Dive into Deep Learning*](https://d2l.ai/)
- François Chollet and Matthew Watson, [*Deep Learning with Python*, 3rd ed.](https://deeplearningwithpython.io/)

## Computer vision

- Antonio Torralba, Phillip Isola and William T. Freeman, [*Foundations of Computer Vision*](https://visionbook.mit.edu/)
- Richard Szeliski, [*Computer Vision: Algorithms and Applications*, 2nd ed.](https://szeliski.org/Book/)

## Text and language models

- Sebastian Raschka, *Build a Large Language Model (From Scratch)*.
- Daniel Jurafsky and James H. Martin, [*Speech and Language Processing*, 3rd-edition draft](https://web.stanford.edu/~jurafsky/slp3/).
- Jay Alammar and Maarten Grootendorst, *Hands-On Large Language Models* (2024).
- Chip Huyen, *AI Engineering: Building Applications with Foundation Models*.

## Tracking

- Roger R. Labbe Jr., [*Kalman and Bayesian Filters in Python*](https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python).
- Alex Bewley et al., [*Simple online and realtime tracking* (SORT)](https://arxiv.org/abs/1602.00763).

For optional background on tracking benchmarks, see [MOTChallenge](https://motchallenge.net/).