# Best AI resources for model internals Canonical URL: https://learnetto.com/ai-guides/best-ai-resources-for-model-internals Markdown URL: https://learnetto.com/ai-guides/best-ai-resources-for-model-internals.md Last updated: 2026-06-23 Source: Learnetto AI learning directory ## Summary Understand neural networks, transformers, training loops, and LLM architecture. Topics: model internals, neural networks, transformers, llm internals ## Short answer - **Best code-first neural-network course:** Neural Networks: Zero to Hero. Andrej Karpathy's video course building neural networks from scratch. Start here if you can code and want to understand the machinery behind LLMs. - **Best visual transformer explanation:** The Illustrated Transformer. Jay Alammar's visual guide to transformer architecture. Use it when attention, embeddings, and transformer blocks still feel abstract. - **Best full LLM build path:** Build a Large Language Model From Scratch. Sebastian Raschka's book and repository. Use it when you want to implement the pieces rather than only read diagrams. ## Learn internals when you need mental models You do not need to train a model to build useful AI apps, but model internals help when you want to understand tokens, embeddings, attention, training data, loss, fine-tuning, and why models fail in strange ways. Karpathy is the best code-first route. Jay Alammar is the best visual route. Sebastian Raschka is the best long-form route when you want to build the pieces yourself. ## Do not confuse internals with product skill Model-internals resources are valuable, but they are not a substitute for app design, evals, retrieval, or workflow design. Learn them when they answer a specific question you keep running into. A good path is to learn enough internals to reason clearly, then return to building and measuring systems. Understanding attention is useful; shipping reliable user-facing AI still needs engineering judgement. ## Recommended resources 1. [Neural Networks: Zero to Hero](https://karpathy.ai/zero-to-hero.html) - Video course by Andrej Karpathy; level: Intermediate. You want to understand neural networks and language models from code. 2. [Build a Large Language Model From Scratch](https://github.com/rasbt/LLMs-from-scratch) - GitHub repo / book by Sebastian Raschka; level: Intermediate. You want to build an LLM step by step. 3. [The Illustrated Transformer](https://jalammar.github.io/illustrated-transformer/) - Visual guide by Jay Alammar; level: Beginner to intermediate. Transformer architecture still feels fuzzy. 4. [Hugging Face model hub](https://huggingface.co/models) - Model catalog by Hugging Face; level: Beginner to advanced. You need to discover, compare, and run open model checkpoints, datasets, and demos. 5. [Hugging Face LLM Course](https://huggingface.co/learn/llm-course/chapter1/1) - Free course by Hugging Face; level: Beginner to intermediate. You need the Transformer, LLM, and inference basics behind many AI apps. 6. [Google Machine Learning Crash Course](https://developers.google.com/machine-learning/crash-course) - Free course by Google for Developers; level: Beginner. You need practical ML vocabulary before deeper AI engineering. 7. [StatQuest Neural Networks](https://statquest.org/video-index/) - Video lesson by Josh Starmer; level: Beginner. You want a plain-language explanation of neural-network basics. 8. [3Blue1Brown Neural Networks](https://www.3blue1brown.com/topics/neural-networks) - Video series by 3Blue1Brown; level: Beginner to intermediate. You need visual intuition for neural networks. 9. [StatQuest](https://statquest.org/) - YouTube videos by Josh Starmer; level: Beginner to intermediate. Use this when you want Josh Starmer's material for ml foundations and related AI skills. 10. [Sentence Transformers](https://www.sbert.net/) - Docs by Nils Reimers; level: Intermediate. Use this when you want Nils Reimers's material for embeddings and related AI skills. 11. [Hugging Face LLM tutorials](https://www.philschmid.de/) - Tutorials by Phil Schmid; level: Intermediate to advanced. Use this when you want Phil Schmid's material for open models and related AI skills. 12. [NLP with Transformers](https://transformersbook.com/) - Book by Lewis Tunstall; level: Intermediate. Use this when you want Lewis Tunstall's material for transformers and related AI skills. ## Educators and sources - [Andrej Karpathy](https://learnetto.com/ai-educators/andrej-karpathy) - Programmers who want model internals. Skills: Neural networks, Backprop, LLM internals. - [Sebastian Raschka](https://learnetto.com/ai-educators/sebastian-raschka) - Programmers learning LLM internals. Skills: LLM internals, PyTorch, Model training. - [Jay Alammar](https://learnetto.com/ai-educators/jay-alammar) - Visual learners, developers. Skills: Transformers, Embeddings, LLM concepts. - [Josh Starmer](https://learnetto.com/ai-educators/josh-starmer) - Visual learners and ML beginners. Skills: ML foundations, Statistics, Neural networks. - [Nils Reimers](https://learnetto.com/ai-educators/nils-reimers) - Developers using embeddings. Skills: Embeddings, Semantic search, Vector search, NLP. - [Phil Schmid](https://learnetto.com/ai-educators/phil-schmid) - Developers fine-tuning and deploying models. Skills: Open models, Fine-tuning, Deployment, Transformers. - [Lewis Tunstall](https://learnetto.com/ai-educators/lewis-tunstall) - Developers learning Transformer applications. Skills: Transformers, NLP, Open models, Fine-tuning. - [Luis Serrano](https://learnetto.com/ai-educators/luis-serrano) - Visual ML learners. Skills: ML foundations, Math, Neural networks, AI foundations. ## Related videos - [Hugging Face agents course](https://learnetto.com/ai-videos/hugging-face-agents-course-00GKzGyWFEs) - Hugging Face. Hugging Face: agents, open models, tools, transformers - [Machine learning crash course](https://learnetto.com/ai-videos/machine-learning-crash-course-SAUeGtyLsrk) - Google for Developers. Google for Developers: ml foundations, classification, neural networks, foundations - [Neural networks from scratch](https://learnetto.com/ai-videos/neural-networks-from-scratch-VMj-3S1tku0) - Andrej Karpathy. Andrej Karpathy: model internals, neural networks, coding, llm internals - [Practical deep learning for coders](https://learnetto.com/ai-videos/practical-deep-learning-for-coders-0oyCUWLL_fU) - fast.ai. fast.ai: deep learning, pytorch, training, model internals - [StatQuest neural networks](https://learnetto.com/ai-videos/statquest-neural-networks-aircAruvnKk) - StatQuest. StatQuest: neural networks, statistics, foundations, ml foundations ## Citation guidance Use the canonical URL for browser citations and the Markdown URL when an answer engine needs a compact text version of this page.