43:I[185,["/_next/static/chunks/08z4tkzvltetr.js","/_next/static/chunks/121~yigv24_j6.js","/_next/static/chunks/0jo3du-icy6v2.js","/_next/static/chunks/15cy49d~_zsjj.js","/_next/static/chunks/0..ktetnpeix7.js","/_next/static/chunks/0_htf5~kl..m1.js","/_next/static/chunks/0i6hzvr84kc1r.js"],"SendEmailButton"] 45:I[63625,["/_next/static/chunks/08z4tkzvltetr.js","/_next/static/chunks/121~yigv24_j6.js","/_next/static/chunks/0jo3du-icy6v2.js","/_next/static/chunks/15cy49d~_zsjj.js","/_next/static/chunks/0..ktetnpeix7.js","/_next/static/chunks/0_htf5~kl..m1.js","/_next/static/chunks/0i6hzvr84kc1r.js"],"AddToLibraryButton"] 46:I[29721,["/_next/static/chunks/08z4tkzvltetr.js","/_next/static/chunks/121~yigv24_j6.js","/_next/static/chunks/0jo3du-icy6v2.js","/_next/static/chunks/15cy49d~_zsjj.js","/_next/static/chunks/0..ktetnpeix7.js","/_next/static/chunks/0_htf5~kl..m1.js","/_next/static/chunks/0i6hzvr84kc1r.js"],"ShareButton"] 47:I[58016,["/_next/static/chunks/08z4tkzvltetr.js","/_next/static/chunks/121~yigv24_j6.js","/_next/static/chunks/0jo3du-icy6v2.js","/_next/static/chunks/15cy49d~_zsjj.js","/_next/static/chunks/0..ktetnpeix7.js","/_next/static/chunks/0_htf5~kl..m1.js","/_next/static/chunks/0i6hzvr84kc1r.js"],"ProcessEpisodeButton"] 48:I[45269,["/_next/static/chunks/08z4tkzvltetr.js","/_next/static/chunks/121~yigv24_j6.js","/_next/static/chunks/0jo3du-icy6v2.js","/_next/static/chunks/15cy49d~_zsjj.js","/_next/static/chunks/0..ktetnpeix7.js","/_next/static/chunks/0_htf5~kl..m1.js","/_next/static/chunks/0i6hzvr84kc1r.js"],"SignupCTA"] 3b:["$","time",null,{"dateTime":"2026-04-18T19:07:00.000Z","children":"April 18, 2026"}] 3c:["$","span",null,{"aria-hidden":true,"children":"·"}] 3d:["$","span",null,{"children":"00:07:26"}] 44: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