CS189 self-study run: Convolutional Neural Networks 🧠📚
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Awesome Math is a comprehensive collection of math resources in a single repository.
It includes materials from Khan Academy, MIT OpenCourseWare, lecture notes, textbooks, and other free resources covering various areas of mathematics.
The project is active and quite popular, currently boasting over 16,000 stars on GitHub.
https://github.com/rossant/awesome-math
It includes materials from Khan Academy, MIT OpenCourseWare, lecture notes, textbooks, and other free resources covering various areas of mathematics.
The project is active and quite popular, currently boasting over 16,000 stars on GitHub.
https://github.com/rossant/awesome-math
GitHub
GitHub - rossant/awesome-math: A curated list of awesome mathematics resources
A curated list of awesome mathematics resources. Contribute to rossant/awesome-math development by creating an account on GitHub.
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Forwarded from Machine Learning
📚 "Natural Language Processing and Large Language Models" is a new open-access book from Springer, written by Chengqing Zong, Yang Zhao, and Yanjun Ma.
It's almost 400 pages long and provides an introduction to modern natural language processing and large language models.
Inside, you'll find information on: neural networks, distributed representations, language models, Transformers, BERT, GPT, tokenization, sentiment analysis, information extraction, text summarization, natural language understanding, machine translation, question answering, and RLHF.
In my opinion, this is a good reference guide for those who want to understand these topics without a very high barrier to entry. I would recommend it. ✨
https://link.springer.com/book/10.1007/978-981-92-0682-7
#NLP #LLM #ArtificialIntelligence #MachineLearning #DataScience #TechBooks
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It's almost 400 pages long and provides an introduction to modern natural language processing and large language models.
Inside, you'll find information on: neural networks, distributed representations, language models, Transformers, BERT, GPT, tokenization, sentiment analysis, information extraction, text summarization, natural language understanding, machine translation, question answering, and RLHF.
In my opinion, this is a good reference guide for those who want to understand these topics without a very high barrier to entry. I would recommend it. ✨
https://link.springer.com/book/10.1007/978-981-92-0682-7
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🔖 Machine Learning in Visualizations
On ML Visualized, you can literally observe how models are trained and how their behavior changes throughout the process.
This format greatly simplifies understanding of algorithms: less abstraction, more clarity.
https://ml-visualized.com/
On ML Visualized, you can literally observe how models are trained and how their behavior changes throughout the process.
This format greatly simplifies understanding of algorithms: less abstraction, more clarity.
https://ml-visualized.com/
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Forwarded from Data Analytics
Updated CS 8803 "Large Language Model" course at Georgia Tech for 2026.
The list of materials covers pre-training, Mixture of Experts (MoE), reasoning, reinforcement learning and self-play, agents, long context, scaling during inference, diffusion language models, safety, interpretability, and much more.
- https://cocoxu.github.io/CS8803-LLM-spring2026/
- https://docs.google.com/spreadsheets/d/1Oisf4imoNL3fs4UWGYAUlMCuYfCACMHCDb0iqEYU8wc/edit?usp=sharing
The list of materials covers pre-training, Mixture of Experts (MoE), reasoning, reinforcement learning and self-play, agents, long context, scaling during inference, diffusion language models, safety, interpretability, and much more.
- https://cocoxu.github.io/CS8803-LLM-spring2026/
- https://docs.google.com/spreadsheets/d/1Oisf4imoNL3fs4UWGYAUlMCuYfCACMHCDb0iqEYU8wc/edit?usp=sharing
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🤖Still Scared AI Is Coming For Your Job?
Here's 20 Repos To Actually Do Something About It
1. Prompt-Engineering-Guide
Explains how to actually get useful, reliable output from AI models instead of guessing and hoping.
2. AI-For-Beginners
Covers the broader AI landscape, not just LLMs, so you're not caught off guard by the next big shift either.
3. generative-ai-for-beginners
A calm, lesson-by-lesson intro to building with generative AI, no ML background assumed.
4. anthropic-cookbook
Real code examples for building things with Claude, straight from the people who make it.
5. openai-cookbook
The same idea for OpenAI's models, practical patterns instead of theory.
6. ML-For-Beginners
Twelve weeks of classic machine learning basics, useful groundwork before jumping into AI specifically.
7. llama_index
Handles connecting AI models to your own data and documents, a skill that comes up constantly in real jobs now.
8. ollama
Lets you run AI models on your own machine, good for understanding them without relying on an API key.
9. llama.cpp
Shows what's actually happening when a model runs, right down to the hardware level.
10. transformers
The library behind most modern AI models, worth exploring even just to see how they're built.
11. awesome-ai-agents
A running list of AI agent projects and frameworks, handy for keeping track of a space that moves fast.
12. nanoGPT
A small, readable implementation of GPT, built by Andrej Karpathy so you can see the whole thing without getting lost.
13. AutoGPT
One of the earliest autonomous AI agent projects, still useful for understanding how agents plan and act.
14. gpt-engineer
An AI agent that writes entire codebases from a prompt, worth trying just to see what it's actually capable of.
15. OpenHands
An open source AI coding agent, a good look at where AI-assisted development is heading.
16. awesome-machine-learning
A broad, well-organized list of ML resources and libraries across every language, a solid reference to keep bookmarked.
17. langchain
The most widely used framework for wiring AI models into actual applications, worth knowing even if you end up not using it.
18. Awesome-LLM
A curated list of papers, models, and tools, good for going deeper once the basics feel comfortable.
19. applied-ml
Real-world write-ups from companies on how they actually use ML and AI in production, not just in demos.
20. llm-course
A structured path from "what is an LLM" to actually fine-tuning and deploying one yourself.
https://t.iss.one/CodeProgrammer✈️
Here's 20 Repos To Actually Do Something About It
1. Prompt-Engineering-Guide
Explains how to actually get useful, reliable output from AI models instead of guessing and hoping.
2. AI-For-Beginners
Covers the broader AI landscape, not just LLMs, so you're not caught off guard by the next big shift either.
3. generative-ai-for-beginners
A calm, lesson-by-lesson intro to building with generative AI, no ML background assumed.
4. anthropic-cookbook
Real code examples for building things with Claude, straight from the people who make it.
5. openai-cookbook
The same idea for OpenAI's models, practical patterns instead of theory.
6. ML-For-Beginners
Twelve weeks of classic machine learning basics, useful groundwork before jumping into AI specifically.
7. llama_index
Handles connecting AI models to your own data and documents, a skill that comes up constantly in real jobs now.
8. ollama
Lets you run AI models on your own machine, good for understanding them without relying on an API key.
9. llama.cpp
Shows what's actually happening when a model runs, right down to the hardware level.
10. transformers
The library behind most modern AI models, worth exploring even just to see how they're built.
11. awesome-ai-agents
A running list of AI agent projects and frameworks, handy for keeping track of a space that moves fast.
12. nanoGPT
A small, readable implementation of GPT, built by Andrej Karpathy so you can see the whole thing without getting lost.
13. AutoGPT
One of the earliest autonomous AI agent projects, still useful for understanding how agents plan and act.
14. gpt-engineer
An AI agent that writes entire codebases from a prompt, worth trying just to see what it's actually capable of.
15. OpenHands
An open source AI coding agent, a good look at where AI-assisted development is heading.
16. awesome-machine-learning
A broad, well-organized list of ML resources and libraries across every language, a solid reference to keep bookmarked.
17. langchain
The most widely used framework for wiring AI models into actual applications, worth knowing even if you end up not using it.
18. Awesome-LLM
A curated list of papers, models, and tools, good for going deeper once the basics feel comfortable.
19. applied-ml
Real-world write-ups from companies on how they actually use ML and AI in production, not just in demos.
20. llm-course
A structured path from "what is an LLM" to actually fine-tuning and deploying one yourself.
https://t.iss.one/CodeProgrammer
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