Vinay Badhan·3d agoTransformers and Embeddings -How AI Learns to Understand TextTransformers and Embeddings — A Simple Explanation
Thakuriaditya·Oct 28From 2017 to Today: How the Transformer Quietly Rebuilt ItselfEvery modern LLM still looks like the 2017 transformer diagram from the outside, but four of its core parts have been replaced…
Jennifer·Oct 26Memecahkan Cipher Substitusi Menggunakan Pendekatan Transformer berbasis Neural NetworkPemecahan dalam cipher substitusi monoalfabetik tanpa mengetahui kunci dan pesan asli / plaintext masih menjadi tantangan dalam dunia…
JAY JOSHI·Oct 26The Mathematics of a Transformer Explained With Numbers You Can Check by Hand With Geometric…As A Postgrade Student I wonder How actually Transformer and LLM did Store Facts in Vector Spaces and How actually Mathematically and…A response icon2A response icon2
Suguna Kanagaraj·Oct 24How Curiosity Led Me to “Attention Is All You Need” - The Paper Behind TransformersHow does ChatGPT understand our questions? How can AI connect words, understand context, and generate meaningful answers? What happens…
Raza Sherazi·Oct 3Engineering a Custom Sequence-to-Sequence Transformer for Schema-Conditioned Text-to-SQLLarge language models have made natural language database interfaces accessible through zero-shot and few-shot prompting. For production…
Shinapri·Oct 3Aggregating Decoders in a Transformer ModelThe previous experiment made me think about how important the identity of the original token embedding vector is.
Poornad·Sep 15Why Does an LLM Use the Last Hidden State to Predict the Next Token?Suppose the input is:
Prabin Tiwari·Sep 15Inside Mamba: How Input-Dependent SSMs Solve the Transformer BottleneckThe quadratic bottleneck of Transformer has finally met its match. An inside look at the selective SSM architecture.
