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Related News: https://t.co/CApFejyugu
"Crawlee—A web scraping and browser automation library for Python to build reliable crawlers. Extract data for AI, LLMs, RAG, or GPTs. Download HTML, PDF, JPG, PNG, and other files from websites. Works with BeautifulSoup, Playwright, and raw HTTP. Both headful and headless mode. With proxy rotation."
This Github repository is an absolute gold mine for any Machine Learning Practitioner!
Kaggle Solutions! 🚀
A treasure trove of best ideas and solutions shared by top performers in the Kaggle competitions.
Link in the next tweet!
A goldmine of tutorials about Generative AI Agents!
You'll find anything Agents-related in this repository. From simple explanations to the most advanced topics.
Star this repo from @NirDiamantAI:
https://t.co/s4YE4co3jb
The content is organized in the following categories:
1. Beginner-friendly agents
2. Task-specific agents
3. Creative and generative agents
4. Advanced agent architectures
5. Special advanced techniques
As of today, there are 16 individual lessons.
I'd like you to bookmark this, but I'd rather tell you to spend next weekend digging into it.
To CoT or Not CoT?
Great paper that shows chain-of-thought mainly helps math and symbolic reasoning
It only has marginal benefit on other tasks
On MMLU, directly generating the answer without CoT leads to almost identical accuracy as CoT unless the question or model's response contains an equals sign!
This paper proves what we are seeing with o1 models.
The extra cost and time during inference is not worth it in most situations but will help when it comes to hard reasoning prompts
For Math:
1. Introduction to Linear Algebra - Strang
2. A First Course in Probability - Ross
3. Mathematics for Machine Learning
For ML:
1. Introduction to Statistical Learning
2. The Elements of Statistical Learning (little advanced)
For DL:
1. UDL - Prince
RAG vs. Long-Context LLMs
I have yet to see a convincing paper or technical blog showing that long-context LLMs can or will replace RAG.
So far I've seen specific long-context applications where long-context LLMs thrive and current retrieval benchmarks are not convincing.
This new paper reports that longer-context LLMs suffer from diminished focus on relevant information, which is one of the primary issues that a RAG system addresses (i.e., uses more relevant information).
They propose an order-preserving RAG mechanism that improves performance on long-context question answering.
It's not perfect and in fact as retrieved chunks increases the quality of responses go up and then declines. But there is a sweet spot where it can achieve better quality with a lot fewer tokens than long-context LLMs.
I am getting better results for RAG plus the added benefit of efficiency as well. Things could change rapidly but still not counting out RAG for now.
We just released the final two courses of AI Python for Beginners! The complete set of four courses is now available and remains free for a limited time.
They teach how to write code (a) Using AI-assistance, which is where the field is going, and (b) to take advantage of generative AI, which allows you to quickly do valuable things with code.
If you're considering learning to code, AI has made this a great time to jump in. Or if you know someone who is considering learning, please recommend these courses!
https://t.co/lTupltSZkT