Jaimin Patel

21 ♂️♑ •||• 👨🏻‍🎓 Artificial Intelligence and Data Science '24 •||• Crypto Enthusiast 💸 •||• One Piece 🔥 •||• LLMs and DL 🔍

[Jaimin Patel @j_patel080]

@Abdod_The_Dude So that people spend ton of money in fomo and they rack in millions again

[Jaimin Patel @j_patel080]

@ClashRoyale

[Jaimin Patel @j_patel080]

@ClashRoyale Almost 3 hours and still in maintenance. If I don't get merge tactics this time its so over. Uninstall and goodbye 👋

[Akshay 🚀 @akshay_pachaar]

State of the art text-to-speech! 🔊

Chatterbox is the first production-grade open-source TTS model.

Give your AI Agents voice with:

- Sub-200 ms latency
- SoTA zero-shot synthesis
- Emotion & intensity control

100% open-source!

[Jaimin Patel @j_patel080]

@xraders_xyz $PI to the moon 🚀
GCLS6NJTN3XZZ4ZCGIP6V52XKISK6PRLA2HAZXBI5QCWXFYPL46YXQKG

#XRadersAI #XRADERS $XR #PiCoin $Pi

[XRADERS - Trade to Earn (T2E) @xraders_xyz]

Search $PI on https://t.co/aB68g7CAr4.

How to Participate:
1. Like ❤️ & QRT 🔁 this post
2. Search $Pi on XRADERS AI Agent,
and leave YOUR thoughts on $Pi coin with your $PI address (Include hashtags: #XRadersAI #XRADERS $XR #PiCoin $Pi)

🎁🥧Your $PI gift will arrive on Pi day (3.14)

[Atheus Network @Athene_Network]

🔋 Athene Incentivized Testnet: Quests, Airdrops, and More!

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Related News: https://t.co/CApFejyugu

[Jaimin Patel @j_patel080]

$FAR G2E Special Round is LIVE! (Feb.10th–Feb.16th) #GuessFARCANA #XRADERS #FARCANA

[Tom Dörr @tom_doerr]

"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."

[Jaimin Patel @j_patel080]

Cats and Dogs 🐱🐶, and others...
GRAB YOUR $FOOD NOW

https://t.co/wmMlOIl0oB

[maharshi @maharshii]

finally, what i wanted

[Akshay 🚀 @akshay_pachaar]

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!

[Tom Dörr @tom_doerr]

Google Dopamin (don't search for it, just wanted to make it clear that it's from Google)

[ₕₐₘₚₜₒₙ @hamptonism]

Bias Variance Tradeoff Explained

[Santiago @svpino]

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.

[Bindu Reddy @bindureddy]

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

[Romee Panchal @romitheguru]

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

[Daily Dose of Data Science @DailyDoseOfDS_]

Neural networks.

[elvis @omarsar0]

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.

[Andrew Ng @AndrewYNg]

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