Transformers & Attention
The architecture behind LLMs like GPT. The Self-Attention mechanism allows the model to weigh the importance of different words in a sentence relative to each other.
Edit the text to see how the attention matrix size adapts. Values are randomized for this demonstration to visualize the Matrix Multiplication mechanism.
Learn Transformers & Attention on DataCamp
Curated courses and career tracks to take your understanding from this demo to real-world mastery. All links open directly on DataCamp.
Transformer Models with PyTorch
Understand the self-attention mechanism, positional encoding, and how Transformer architectures power modern LLMs like GPT.
Introduction to LLMs in Python
Learn about Large Language Models, how they work, and how to use them effectively using Python APIs.
Working with Hugging Face
Use the Hugging Face `transformers` library to fine-tune BERT, GPT-2 and other pretrained Transformer models for NLP tasks.
Developing LLM Applications with LangChain
Build LLM-powered applications with LangChain, including retrieval-augmented generation (RAG) and AI agents.
Introduction to Generative AI Concepts
Explore the landscape of generative AI including Transformers, VAEs, and diffusion models. Understand how ChatGPT was built.
Natural Language Processing in Python
Build NLP skills from bag-of-words to Transformer-based text classification, summarization, and question answering.
