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The first bot in the world of Telegram that offers free courses, free certificates.
❤2
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.
❤6
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
#NLP #LLM #ArtificialIntelligence #MachineLearning #DataScience #TechBooks
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❤4
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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.
http://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.
http://ml-visualized.com/
❤5
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
❤5