imanj @LearnerShe - Twitter Profile

6 months ago

Make the most of your weekend. Don't sleep on this. Stanford's Autumn 2025 Transformers & LLMs course. 8 lectures. Free. While others scroll, you could understand how Flash Attention achieves 3x speedup, how LoRA cuts fine-tuning costs by 90%, and how MoE makes models efficient. ➕ 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 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.

techNmak's tweet photo. Make the most of your weekend.

Don't sleep on this.

Stanford's Autumn 2025 Transformers & LLMs course. 8 lectures. Free.

While others scroll, you could understand how Flash Attention achieves 3x speedup, how LoRA cuts fine-tuning costs by 90%, and how MoE makes models efficient.

➕ 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

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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