18:I[66739,["/_next/static/chunks/0u0nyy7wb9me2.js?dpl=dpl_EVxQmm5ee6SfSwpTLEi7dkcqj48i","/_next/static/chunks/05hx6kuf6-oy4.js?dpl=dpl_EVxQmm5ee6SfSwpTLEi7dkcqj48i","/_next/static/chunks/12hmyxte1w1dw.js?dpl=dpl_EVxQmm5ee6SfSwpTLEi7dkcqj48i","/_next/static/chunks/166.u_60qj09..js?dpl=dpl_EVxQmm5ee6SfSwpTLEi7dkcqj48i","/_next/static/chunks/0g9_wl34k-6l4.js?dpl=dpl_EVxQmm5ee6SfSwpTLEi7dkcqj48i","/_next/static/chunks/06dn~uer6.43l.js?dpl=dpl_EVxQmm5ee6SfSwpTLEi7dkcqj48i"],"default"] 1a:I[38632,["/_next/static/chunks/0qqtp3r0h7kah.js?dpl=dpl_EVxQmm5ee6SfSwpTLEi7dkcqj48i","/_next/static/chunks/0iukvycbd42rl.js?dpl=dpl_EVxQmm5ee6SfSwpTLEi7dkcqj48i"],"IconMark"] 19:T42b,

This session of the MIT Deep Learning series explores the mechanics behind LLMs, framing them as advanced autoregressive systems for next-token prediction. The technical overview covers the evolution from basic statistical methods to trillion-parameter architectures with massive context windows.

Key takeaways include:

The transition from Bayesian counting to modern self-supervised learning.
How prompt engineering techniques like "Chain of Thought" unlock emergent reasoning.
Architectural innovations like LoRA for efficient fine-tuning.
Critical safety frontiers: Jailbreaks, hallucinations, and alignment through RLHF.

The future of AI lies in agents that don't just predict text but plan and execute tasks via external tools.

All my links: https://linktr.ee/learnbydoingwithsteven #learnbydoingwithsteven #MIT #DeepLearning #LLM #AI #MachineLearning #GenerativeAI #PromptEngineering #DataScience #AIAgent

Understanding Large Language Models: A Technical Deep Dive

Steven AI Talk

Understanding Large Language Models: A Technical Deep Dive