Transformer Blocks: Learning Layers of Meaning

This Stanford lecture series is one of the best ways I've found to really comprehend LLMs and Generative AI (not just copy prompts). Here are the 9 sessions you should watch in this order: Basics of Transformers – https://lnkd.in/gaaRDexT Transformer Models + Useful Tips – https://lnkd.in/gy4FUwNY Transformers → https://lnkd.in/gsPiCrEU is the link to Large Language Models. How to Train LLMs — https://lnkd.in/gvHJvgqP Fine-tuning and adaptation (fine-tuning, etc.) — https://lnkd.in/g6kgtPKR Reasoning in LLMs – https://lnkd.in/gAACSUG6 Agentic LLMs (tools, planning, processes) — https://lnkd.in/gVm6js9z What does "good" truly mean? https://lnkd.in/gJhbFQ4s Recap + What people are talking about right now is https://lnkd.in/g5JMNTsf My advice is to treat it like a mini-bootcamp: watch one video at a time, take notes, and then stop to do tiny experiments after each one. This is a good starting point if you're going to learn LLMs in 2026.

🚀 Stanford just dropped a must-watch for anyone serious about AI 🎓 “CME 295: Transformers & Large Language Models” is now fully available on YouTube — and it’s absolute gold. If you’re building a real AI career, stop scrolling. This is not another surface-level overview. It’s one of the clearest, most structured introductions to LLMs, straight from Stanford’s Autumn 2025 curriculum. 📚 What you’ll learn: • How Transformers actually work (tokenization, attention, embeddings) • Decoding strategies & Mixture of Experts (MoE) • LLM fine-tuning (Supervised, LoRA, RLHF) • Evaluation methods (LLM-as-a-Judge) • Optimization tricks (RoPE, quantization, approximations) • Reasoning & scaling laws • Agentic workflows (RAG, tool calling) 🧠 My workflow: I grab the transcripts → load them into NotebookLM → finish the lectures → then replay them during walks or commutes. That combo seriously boosts retention. 🎥 Watch the lectures here: Lecture 1: https://lnkd.in/gwq8jmzJ Lecture 2: https://lnkd.in/gMkA82p2 Lecture 3: https://lnkd.in/g2Dhr73w Lecture 4: https://lnkd.in/gxXMhXKy Lecture 5: https://lnkd.in/gcEQgjD9 Lecture 6: https://lnkd.in/g376ZvFU Lecture 7: https://lnkd.in/guT-Q3T5 Lecture 8: https://lnkd.in/grw2jrxN Lecture 9: https://lnkd.in/gSksniEY 🗓️ Do yourself a favor for 2026: Block 2 hours per week per lecture and go through them properly. This is how you move from using AI → understanding AI. #AI #LLM #Transformers #Stanford #MachineLearning #ArtificialIntelligence #AICareer #DeepLearning #AgenticAI #RAG #GenerativeAI #LearnAI #FutureOfWork

One of my biggest 2025 takeaways from working with AI: 👉 Don’t just ask GPT. Let GPT ask you. 👈 (By GPT, I mean for all other tools as well: Gemini, Claude, etc.) For example, when working with coding assistants, instead of saying: "Implement this feature" -> Try adding: "Do you have any questions for me?" It’s because many tasks are under-specified, while LLMs are optimized for completion, not clarification. I’ve felt this deeply: when I start from a vague prompt, I often end up iterating through patches and fixes. It was both time-consuming and frustrating. By explicitly inviting questions upfront, my experience has improved a lot. And it has also been a great thinking exercise as it forces me to articulate intent, constraints, and edge cases more precisely. Below is another hand-drawn sketch of mine. I intentionally hid L-L-M inside the AI figure as its current backbone. Hope you like my design. 😉 #AI #LLM #PromptEngineering #ThinkingInPublic

A big theme of this year's reading list was AI. All books were delightful and educational, but I would recommend "AI Engineering" and "Large Language Models: A Deep Dive" for practitioners and those wanting to get into AI. On another front, "Investment Valuation" is invaluable for those looking to pick their own stocks... Keep Learning. Keep growing #2025inBooks

If you really want to understand LLMs. Code and experiment with the following: • Tokenizers Shows how LLMs read text as numbers, not words, why token choices affect cost and context, and how unknown words are handled. Build a BPE tokenizer. • Embeddings Helps you understand how words are represented as vectors, why semantic similarity emerges without explicit rules, and how word positions are encoded. • Attention Helps you understand how models decide what information matters in a sequence, independent of distance. • Causal Attention Helps you understand how LLMs prevent looking at the future, so each token depends only on the past. • Transformers Helps you understand how stacking simple blocks creates complex behavior and why depth matters. • Layer Normalization Helps you understand training stability and why the model fails without proper normalization. • Dropout Helps you understand generalization and why a bit of randomness prevents memorization. • Feed-forward Network Explains how transformers add non-linearity and transform information within each token after attention. Implementing a feed-forward network with an activation function. • Temperature Scaling Helps you understand the confidence vs creativity trade-off during generation. • Top-k Sampling Helps you understand how limiting choices prevents low-probability noise during generation. • Quantization Helps you understand performance vs accuracy trade-offs in real-world deployment. • Pretraining Teaches how models learn language structure and patterns from raw text. • Finetuning Helps you understand how behavior is shaped, even when the base model stays the same. That's it for now. Keep Learning, Keep Sharing, and Keep Growing. #llm #ai #machinelearning #deeplearning

What if machine learning didn’t have to feel overwhelming, and even the toughest ideas could finally click? There was a point in my ML journey when concepts felt heavier than they needed to be. Not because the models were impossible to understand, but because the explanations weren’t. Somewhere between research papers, jargon, threads, and buzzwords, clarity just… disappeared. So I slowed down. I started writing things out by hand—one model at a time. I rebuilt the intuition, sketched the diagrams, explored the purpose behind each idea, and asked why it still matters in 2025. That process became a habit. And now, it’s become a series. I want to make machine learning feel simple without making it shallow. Clear without dumbing it down. Sophisticated, but still human. Across this series, I’ll share my handwritten notes on models like transformers, diffusion models, graph neural networks, reinforcement learning, neural ODEs, meta-learning, autoencoders, ensemble learning, multimodal models, and more. Each note emphasizes bottom-line intuition and visual understanding, so the ideas feel lighter, approachable, and actionable. To kick things off, I’m attaching an overview set of notes that captures the essence of these models: what they are, why they matter, and where they shine. And if my diagrams look a little… abstract at times, please be kind. The models are smart, my handwriting is still learning. If ML has ever felt overwhelming, I hope this series creates the opposite feeling: calm, clarity, and curiosity. One model at a time. Let’s begin. #MachineLearning #MLSimplified #DeepLearning #AI #Transformers #GraphNeuralNetworks #ReinforcementLearning #MetaLearning #DiffusionModels #NeuralODEs #Autoencoders #EnsembleLearning #MultimodalAI #MLForHumans #DataScience #LearnML #AI2025 #TechMadeSimple #VisualLearning #MLNotes

Working with LLM-based software feels different. My colleagues keep hearing the same questions from customers: "Why does it sometimes give different answers to the same question?" or "It worked 99 times, then failed on the 100th -- why?" After a few years of taming these systems in production, I decided to write down what I've learned. Part 1 asks: Is there fundamental randomness in LLMs? The answer is "no, but yes" -- technically deterministic, but production systems introduce variance you can't control. Part 2 explores the bigger source of unpredictability: generalization. Every input to an LLM is underspecified. The model fills in gaps using its own understanding, and you don't see the fill-in until it surprises you. I use the analogy of floor grating: it looks solid, your tests walk across it fine, then production traffic brings a differently-shaped problem and it falls through. The posts include real cases from production; a search system that decided a university must be biomedical, an LLM that invented commands, a chatbot that distrusted ski shop hours. If you work with LLMs in production, I hope these observations are useful. Part 1: https://lnkd.in/dTq7qvjP Part 2: https://lnkd.in/daepcw2u #LLM #NLP #MachineLearning #AI

DeepSeek AI just addressed a long standing stability issue in #AI deep learning architectures, and they laid it out in the paper “Manifold Constrained Hyper Connections”, where a 1967 algorithm becomes the key that keeps the whole system under control. When deep learning took off, researchers hit a wall, you cannot keep stacking layers because signals and gradients either explode or vanish, so training breaks down long before you reach the depth you want. ResNets changed the game in 2016 with residual connections, each block learns a correction and you add it back to the input, so there is always a clean path for information to travel through many layers. More recently, people asked a natural follow up, what if we had multiple such paths instead of one. Hyper Connections do this by running several parallel streams and learning how to mix them at every layer with small matrices. The gains can be real, but the risk is structural, those mixing matrices compound across depth. Even a small 5% amplification per layer becomes about 18x after 60 layers, and the paper reports amplification reaching about 3000x, which is where training collapses. DeepSeek move is to make mixing stable by construction. They use the Sinkhorn Knopp algorithm to constrain the mixing matrices to be doubly stochastic, meaning each row and each column sums to 1, so the mix behaves like controlled redistribution rather than an amplifier. With that constraint, the reported instability drops from 3000x down to about 1.6x, with only about 6.7 percent additional training overhead, and the big idea is that stability becomes a guaranteed property of the math instead of something you manage case by case.

What a gift to wrap up 2025 and kickstart 2026 for anyone serious about AI. 🚀 Stanford just dropped one of the best “new year upgrades” you can give your AI career. 🎓 CME 295: Transformers & Large Language Models (Autumn 2025) is now fully available on YouTube, straight from Stanford’s official curriculum. ( https://lnkd.in/gg-E2JWS ) Nine deep, structured lectures no fluff, no hot takes. If you’re building in AI, stop scrolling. This is the clearest, most practical foundation on LLMs you’ll find right now. Topics includes .... 1. How Transformers actually work: tokenization, attention, embeddings, positional encodings 2. Transformer-based models & tricks: RoPE, ALiBi, sparse attention, BERT and variants 3. Decoding & MoEs: sampling strategies, temperature, beam search, dense vs sparse MoE 4. LLM training & finetuning: pretraining, SFT, LoRA, RLHF, parameter-efficient tuning 5. Evaluation: LLM-as-a-judge and practical eval frameworks 6. Optimization: quantization, KV cache, hardware-aware tricks, scaling laws 7. Reasoning & agentic workflows: chain-of-thought, RAG, tool calling, agentic LLMs How to study it 🧠 My workflow: Grab the YouTube transcripts and load them into NotebookLM for Q&A and summaries. Do focused viewing first, then replay the lectures during walks/commutes to reinforce concepts. This combo massively boosts retention over just “watching another playlist”. Start with these lectures 🗓️ Do yourself a favor for 2026: block 1–2 hours per day per lecture, and actually work through them. If you’re in AI infra, agents, or apps this is the one course you don’t want to skip. Let’s level up. #AI #LLMs #Transformers #GenAI #Stanford