AI & Large Language Models (LLMs) reading list (Articles, Blogs, Threads, Videos)
Hello, I am Louis Lebbos (Linkedin, Twitter), a curious tech founder.
I created this page in 2022 as part of my continued learning journey into ML/AI and LLMs and thought I would share it here for anyone to find.
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https://twitter.com/i/lists/1623617160613355528
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Large Language Model (LLM) books:
These are some of the best LLM books I found:
Hands on Large Language Models: Jay Alammar and Maarten Grootendorst
Stephen Wolfram: What is ChatGPT and why does it work?: Super simple guide into how language models work.
Sebastian Raschka: Build a Large Language Model (From Scratch)
📱Non technical Overviews
Andrej Karpathy does a GREAT presentation on how GPTs work, link here for the video on Microsoft.com
AI for the next ERA: Overview of advancements and opportunities with Sam Altman(OpenAI)
Simple explanation of how ChatGPT works & its components
Another Andrej Karpathy required Watch
This article Is a decent intro for beginners:
ChatGPT is a blurry JPEG of the web
New york times with a tiny bit more technical view of how these models work. (Watch an A.I. Learn to Write by Reading...)
💻Introductions (Mildly technical)
Andrej Karpathy does a broad overview of how Large Language Models are built (State of GPT) [Now linked above as well because it is a very good intro too]
Language modelling at scale: Gopher, ethical considerations and retrieval
Elad Gil: AI Revolution - Transformers and Large Language Models (LLMs)
ScaleAI guide to Large language models
Articles (intermediate)What is GhatGPT doing and how does it work? (Stephen Wolfram)
(This article is Very long thankfully it is also printed as a Book and a good intro book about LLMs at that)Efficient Estimation of Word Representations in Vector Space
💬LLM Specific Courses
Cohere LLM University: cohere.com/docs/llmu
DeepLearning.AI Short Course on Prompt Engineering
Full Stack Deep Learning Free LLM Bootcamp
Sandpits/Playgrounds:
Google Explorables
🎧Podcast episodes about Large Language Models and AI:
Jensen Huang CEO of NVIDIA on No Priors (No priors quickly became one of my favorite pods, great other episodes: Noam Brown, Matei Zaharia)
Oriol Vinyals: Deep Learning and AGI
Dr. ANDREW LAMPINEN (Deepmind) - Natural Language, Symbols and Grounding
Dataframed #96 GPT-3 and our AI-Powered Future (July 2022)
Towards Data Science 129. Amber Teng - Building apps with a new generation of language models (Oct 2022)
Vector Databases for Machine Learning. Pinecone on Practical AI
Videos
Adrian Gomez on the potential of LLMs
What the full ALphaGo Documentary on YouTube:
The moment AlphaGo beat Lee Seedol at Go was the spark for most people of this latest AI spring.
Greg Brockman: Future of LLMs
Recently the most impressive work on Game AI has been the work of the META FAIR team on CICERO:
Watch the CICERO videos here
Cicero article and paper here
4 - Transformers
Technical Fundamentals
1- Embeddings
Dharmesh’s post about Embeddings: Guide to vector embeddings
DataStax What we learned building a vector Database
What is a neural network
3- Language Models explained
2- Neural Networks
Videos to watch about large language models, NLP, AI, AGI, etc
Limitations of LLMs (and what’s next in AI)
Francois Chollet on what real general intelligence means and the ARC benchmark