Bookmarks for tech professionals and enthusiasts at Tech Trendsetters. Constantly updated and curated collection of literature, courses, and resources covers the latest trends and essential skills.
Machine Learning / AI related
Courses below require Python, Fundamentals of Machine Learning, Basic Probability and Statistics, Linear Algebra
CS224N: Natural Language Processing with Deep Learning
https://web.stanford.edu/class/cs224n/
Cool Stanford course, updated every year. This year, for the first time, they decided not to post the lectures on YouTube, although all the 2023 lectures remained publicly available – I highly recommend them.
Chris Manning – notes
https://web.stanford.edu/class/cs224n/readings/cs224n-self-attention-transformers-2023_draft.pdf
https://web.stanford.edu/class/cs224n/readings/
The teacher of the course above and one of the most successful scientists, authors of research papers without a large computer (DPO, Backpack language models), Chris Manning, posts all lecture materials in the public domain. Based on the update dates, it is clear that the updated materials are for the 2024 course, use it!
Dan Jurafsky — Speech and Language Processing (3rd ed. draft)
https://web.stanford.edu/~jurafsky/slpdraft/
The author of the main textbook on NLP over the past 20 years, also from Stanford, Dan Jurafsky continues to make new chapters of the textbook publicly available, constantly updating old ones. In general, this is practically the only book that you can read in its entirety and already have the keys to understanding 80% of what is happening in the industry.
The textbook was last updated on January 5, 2024.
Transformers United
https://web.stanford.edu/class/cs25/prev_years/2023_winter/index.html
https://www.youtube.com/playlist?list=PLoROMvodv4rNiJRchCzutFw5ItR_Z27CM
The second most important course is to understand what's going on - with a general focus on NLP, CV and multimodal models.
CS236: Deep Generative Models
https://deepgenerativemodels.github.io/
https://www.youtube.com/watch?v=XZ0PMRWXBEU
The introduction to deep generative models and theoretical analysis of any aspect of existing deep generative models. It touches on difficult concepts such as how to evaluate a generative model. The course materials, including lecture slides and notes, are publicly available and updated regularly. It's an excellent resource for anyone seeking to build a strong foundation from a very beginning.
👉 All in one on my resource hub: https://iwooky.substack.com/p/learning-resource-hub
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