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8 entries with this tag
Three more Python demos for the AI Papers Explained series. Compare base T5 with instruction-tuned FLAN-T5, see Chain-of-Thought prompting in action, and visualize the scaling laws that reshaped the entire AI industry.
The paper that bridged pretraining and ChatGPT. Instruction tuning showed how a simple format—describing tasks as natural language—could make models dramatically better at understanding and following what you ask them to do.
A companion guide to our AI Papers Explained series. Three Python scripts that bring the concepts from Attention, BERT, and GPT-2 to life with real models you can run on your laptop.
A beginner-friendly explanation of GPT-2 (2019), the paper that showed AI could write coherent, creative text by simply predicting the next word. Part 3 of our AI Papers Explained series.
A beginner-friendly explanation of BERT (Bidirectional Encoder Representations from Transformers), the 2018 paper that taught AI to understand language by reading in both directions. Follow-up to our 'Attention Is All You Need' explainer.
Completed comprehensive AI research article series: Attention Is All You Need, BERT, and GPT-2. Established foundational understanding of modern language models through accessible explainers.
A beginner-friendly explanation of the groundbreaking 'Attention Is All You Need' paper that introduced Transformers. Learn what attention mechanisms are, why they matter, and how they power modern AI like ChatGPT.
Evolving synthesis of the Transformer architecture — from attention mechanisms through BERT, GPT, scaling laws, and modern LLMs