AI educator

Jay Alammar

The Illustrated Transformer

Still one of the clearest visual explanations of transformer concepts.

Start with: Read The Illustrated Transformer, then The Illustrated GPT-2.

Resources from Jay Alammar

Visual guide

The Illustrated Transformer

Beginner to intermediate

Transformer architecture still feels fuzzy.

Skills

Learner questions

Who should learn from Jay Alammar?

Visual learners, developers should start here when they need transformers, embeddings, and llm concepts. The strongest fit is a learner who wants material in these formats: visual essays.

What should I do first?

Read The Illustrated Transformer, then The Illustrated GPT-2. After that, open one related resource below and write down the exact workflow, concept, or implementation pattern you want to apply.

What problem does this help with?

Still one of the clearest visual explanations of transformer concepts. Use this profile when you are comparing educators by topic, level, format, and practical usefulness rather than browsing random AI content.

How do I compare this with other educators?

Compare the skill coverage, the starting recommendation, the educator's own resources, and any videos when available. If you need transformers, search the directory for that skill and shortlist three profiles before committing to a course, book, or playlist.

More related resources

Resource	Kind	Level	Use when
Multi-Vector Embedding Models with Sentence Transformers Hugging Face	Practical guide	Intermediate to advanced	You want a runnable guide to ColBERT-style late-interaction retrieval, including MaxSim scoring, retrieve-and-rerank, indexing, visual document retrieval, evaluation, and the storage-quality tradeoff versus dense embeddings.
Training and Finetuning Multi-Vector Embedding Models with Sentence Transformers Hugging Face	Training guide	Advanced	You need an end-to-end August 26, 2026 recipe for adapting a late-interaction retriever to your own domain, from datasets and losses through training, evaluation, and index optimization.
Cohere models overview Cohere	Model docs	Beginner to advanced	You need to choose between current Cohere Command, embedding, and rerank models for grounded enterprise search.
Qwen Model Studio model list Qwen	Model catalog	Intermediate	You need the current hosted Qwen and third-party model catalog with modality coverage and capability splits.
Cohere Embed guide Cohere	Guide	Intermediate	You want Cohere's official embedding guidance for multilingual retrieval, semantic search, and RAG ingestion choices.
Cohere Embed Jobs API Cohere	API docs	Intermediate	You need Cohere's bulk embedding workflow for larger corpora instead of issuing one embedding request per document at ingestion time.
Perplexity Embeddings API Perplexity	API docs	Intermediate	You want the official Perplexity embeddings entry point before designing retrieval, semantic search, or hybrid RAG pipelines around its newer embedding models.
Perplexity Cookbook Perplexity	Cookbook	Intermediate	You want copyable Perplexity examples for grounded search, embeddings, and end-to-end RAG work instead of assembling the workflow from scattered reference pages.
RAG with Perplexity Embeddings Perplexity	Guide	Intermediate	You want Perplexity's practical guidance for turning its embeddings and search stack into a real RAG workflow instead of reading only low-level API docs.
Hugging Face model hub Hugging Face	Model catalog	Beginner to advanced	You need to discover, compare, and run open model checkpoints, datasets, and demos.
Hugging Face LLM Course Hugging Face	Free course	Beginner to intermediate	You need the Transformer, LLM, and inference basics behind many AI apps.
Pinecone Learn: Retrieval-Augmented Generation Pinecone	Guide	Beginner to intermediate	You need to understand the moving parts of RAG.