Uploaded January 2025 | Updated September 2026, 5 hours ago

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Tired of LLM overwhelm? This video cuts through the noise and provides a clear roadmap for your large language models journey.

Choose your path:

◉ Practitioner: Focus on applications, fine-tuning, prompt engineering, and building real-world AI systems. Learn key libraries like LangChain and Hugging Face, leverage OpenAI/Cohere APIs, and master Retrieval Augmented Generation (RAG).
Projects for practitioners:
• Cold Email Generator with Llama 3.1 (youtube.com/watch?v=CO4E_9V6li0)
• Conversational Agent (https://pub.aimind.so/build-and-deploy-your-first-conversational-document-retrieval-agent-using-langchain-and-streamlit-aaa1ae852b96)
• Advanced Hybrid Search (github.com/Rman410/hybrid-search/tree/main)

◉ Researcher: Delve into the internals of LLMs. Explore foundational papers like "Attention is All You Need," build your own mini-Transformer, and master advanced fine-tuning techniques like Lora and PEFT.
Essential LLM papers:
• Attention Is All You Need (arxiv.org/pdf/1706.03762)
• Language Models are Few-Shot Learners (arxiv.org/abs/2005.14165)
• Scaling Laws for Neural Language Models (arxiv.org/abs/2001.08361)
Projects for researchers:
• Implementing Transformers From Scratch Using PyTorch (kaggle.com/code/arunmohan003/transformer-from-scratch-using-pytorch)
• Byte Pair Encoding (github.com/teleprint-me/byte-pair/blob/main/README.md)
- Paper: Neural Machine Translation of Rare Words with Subword Units (arxiv.org/abs/1508.07909v5)
- Lei Mao’s Tutorial: Byte Pair Encoding (leimao.github.io/blog/Byte-Pair-Encoding/)
• LLM Fine-Tuning (github.com/roy-sub/LLM-FineTuning)


This video includes:
◉ Practical advice: Project ideas, tool recommendations, and actionable steps for both paths.
◉ Key concepts: Prompt engineering, RAG, fine-tuning, transformers, attention mechanisms, and more.
◉ Expert insights: Tips for navigating the LLM landscape and avoiding common pitfalls.

Watch now and start your LLM journey with confidence!

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Timeline:

Intro: (0:00​​​)
Practitioner vs. Researcher: (0:13​​​)
The practitioner path: (0:44​​​)
The researcher path: (3:40​​​)
What you should do today: (5:43​​​)
LLM craze is overhyped: (8:34​​​)

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About The Platform:

StrataScratch (platform.stratascratch.com/coding?code_type=2&page_size=100&utm_source=youtube&utm_medium=click&utm_campaign=YT+llm+deep+dive) is a platform that allows you to practice real data science interview questions. There are over 1000+ interview questions that cover coding (SQL and Python), statistics, probability, product sense, and business cases.

So, if you want more interview practice with real data science interview questions, visit platform.stratascratch.com/coding?code_type=2&page_size=100&utm_source=youtube&utm_medium=click&utm_campaign=YT+llm+deep+dive. All questions are free and you can even execute SQL and Python code in the IDE. Still, if you want to check out the solutions from other users or from the StrataScratch team, you can use ss15 for a 15% discount on the premium plans.

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Contact:

If you have any questions, comments, or feedback, please leave them here!
Feel free to also email us at team@stratascratch.com

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#LLM #LargeLanguageModels #DeepLearning #MachineLearning #AI #ArtificialIntelligence ##DataScience #Developer #DataEngineering #DataAnalyst