@techNmak: Learn LLMs from Stanford this weekend. Stanford's Autumn 2025 Transformers & LLMs course is fully public and 100% free.…

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Stanford University's Autumn 2025 Transformers & LLMs course is fully public and free, covering transformer fundamentals, advanced techniques, LLM training, inference optimization, and more in 9 lectures.

Learn LLMs from Stanford this weekend. Stanford's Autumn 2025 Transformers & LLMs course is fully public and 100% free. In 9 lectures, you’ll unlock the exact mechanics behind: • Flash Attention (3x faster models) • LoRA (90% cheaper fine-tuning) • Mixture of Experts (Massive efficiency scaling) What's covered: Lecture 1: Transformer Fundamentals → Tokenization and word representation → Self-attention mechanism explained → Complete transformer architecture → Detailed implementation example Lecture 2: Advanced Transformer Techniques → Position embeddings (RoPE, ALiBi, T5 bias) → Layer normalization and sparse attention → BERT deep dive and finetuning → Extensions of BERT Lecture 3: LLMs & Inference Optimization → Mixture of Experts (MoE) explained → Decoding strategies (greedy, beam search, sampling) → Prompting and in-context learning → Chain-of-thought reasoning → Inference optimizations (KV cache, PagedAttention) Lecture 4: LLM Training & Fine-tuning → Pretraining and scaling laws (Chinchilla law) → Training optimizations (ZeRO, model parallelism) → Flash Attention for 3x speedup → Quantization and mixed precision → Parameter-efficient finetuning (LoRA, QLoRA) Lecture 5: LLM Tuning → Preference tuning → RLHF overview → Reward modeling → RL approaches (PPO and variants) → DPO Lecture 6: LLM Reasoning → Reasoning models → RL for reasoning → GRPO → Scaling Lecture 7: Agentic LLMs → Retrieval-augmented generation → Advanced RAG techniques → Function calling → Agents → ReAct framework Lecture 8: LLM Evaluation → LLM-as-a-judge overview →Best practices and benefits →Biases and pitfalls Lecture 9: Recap & Trending topics From Stanford Online: Rigorous instruction. Latest techniques. Free access. Perfect for: → ML engineers building with LLMs → AI engineers understanding transformers → Researchers working on language models → Anyone learning beyond API calls This weekend: learn the techniques that separate good engineers from great ones. (I will put the playlist in the comments.) Repost to save someone $$$ and a lot of confusion. Follow @techNmak for more AI/ML insights.
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Learn LLMs from Stanford this weekend.

Stanford’s Autumn 2025 Transformers & LLMs course is fully public and 100% free.

In 9 lectures, you’ll unlock the exact mechanics behind: • Flash Attention (3x faster models) • LoRA (90% cheaper fine-tuning) • Mixture of Experts (Massive efficiency scaling)

What’s covered:

Lecture 1: Transformer Fundamentals → Tokenization and word representation → Self-attention mechanism explained → Complete transformer architecture → Detailed implementation example

Lecture 2: Advanced Transformer Techniques → Position embeddings (RoPE, ALiBi, T5 bias) → Layer normalization and sparse attention → BERT deep dive and finetuning → Extensions of BERT

Lecture 3: LLMs & Inference Optimization → Mixture of Experts (MoE) explained → Decoding strategies (greedy, beam search, sampling) → Prompting and in-context learning → Chain-of-thought reasoning → Inference optimizations (KV cache, PagedAttention)

Lecture 4: LLM Training & Fine-tuning → Pretraining and scaling laws (Chinchilla law) → Training optimizations (ZeRO, model parallelism) → Flash Attention for 3x speedup → Quantization and mixed precision → Parameter-efficient finetuning (LoRA, QLoRA)

Lecture 5: LLM Tuning → Preference tuning → RLHF overview → Reward modeling → RL approaches (PPO and variants) → DPO

Lecture 6: LLM Reasoning → Reasoning models → RL for reasoning → GRPO → Scaling

Lecture 7: Agentic LLMs → Retrieval-augmented generation → Advanced RAG techniques → Function calling → Agents → ReAct framework

Lecture 8: LLM Evaluation → LLM-as-a-judge overview →Best practices and benefits →Biases and pitfalls

Lecture 9: Recap & Trending topics

From Stanford Online: Rigorous instruction. Latest techniques. Free access.

Perfect for: → ML engineers building with LLMs → AI engineers understanding transformers → Researchers working on language models → Anyone learning beyond API calls

This weekend: learn the techniques that separate good engineers from great ones.

(I will put the playlist in the comments.)

Repost to save someone $$$ and a lot of confusion. Follow @techNmak for more AI/ML insights.

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