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Technical Synthesis of LLM Learning Resources
- Deep Learning Foundations:* The MIT Deep Learning 2025 course provides essential background for understanding LLMs, as LLMs and GenAI are rooted in deep learning concepts such as neural networks, pretraining, and model architecture.
- LLM Fundamentals and Construction:* Karpathy's LLM 101 and 3Blue1Brown's Transformers Visualized offer fundamental knowledge, while Karpathy's GPT series and Raschka's walkthrough enable building LLMs from scratch, crucial for understanding tokens, transformer architecture, layers, and attention mechanisms.
- Fine-tuning and Merging:* Maxime Labonne's resources detail fine-tuning (adapting pre-trained models) and model merging (combining behaviors from different fine-tuned models), essential for customizing LLMs for specific tasks.
- Production-Ready RAG:* Jerry Liu's work explains Retrieval Augmented Generation (RAG), which injects context from a knowledge base using embeddings, queries, and vector similarity to improve LLM accuracy and reduce hallucinations. Agentic RAG extends this by integrating AI Agents for dynamic context retrieval.
- Inference Optimization:* Mark Moyou's content addresses balancing model size/quality with compute requirements, covering hardware, LLM workings, parallelization challenges, and best practices for scalable LLM solutions.
- Model Context Protocol (MCP):* Anthropic's workshop by Mahesh Murag introduces MCP, a standardized interface for AI Agents to interact with external tools like databases and search engines, using a client-server structure. Groq's LPU chip, detailed by Igor Arsovski, offers a hardware solution to address LLM inference bottlenecks, measured in tokens per second (TPS).
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