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Category: Transformer

  • The Machine Learning “Advent Calendar” Day 24: Transformers for Text in Excel

    The Machine Learning “Advent Calendar” Day 24: Transformers for Text in Excel An intuitive, step-by-step look at how Transformers use self-attention to turn static word embeddings into contextual representations, illustrated with simple examples and an Excel-friendly walkthrough. The post The Machine Learning “Advent Calendar” Day 24: Transformers for Text in Excel appeared first on Towards…

  • RF-DETR Under the Hood: The Insights of a Real-Time Transformer Detection

    RF-DETR Under the Hood: The Insights of a Real-Time Transformer Detection From rigid grids to adaptive attention, this is the evolutionary path that made detection transformers fast, flexible, and formidable. The post RF-DETR Under the Hood: The Insights of a Real-Time Transformer Detection appeared first on Towards Data Science. David Redó Nieto Go to original…

  • An Interactive Guide to 4 Fundamental Computer Vision Tasks Using Transformers

    An Interactive Guide to 4 Fundamental Computer Vision Tasks Using Transformers An overview of 4 fundamental computer vision tasks – image classification, image segmentation, image captioning and visual question answering, with transformer models. Compare ViT, DETR, BLIP, and ViLT performance interactively by providing a practical Streamlit app implementation guide. The post An Interactive Guide to…

  • Learn How to Use Transformers with HuggingFace and SpaCy

    Learn How to Use Transformers with HuggingFace and SpaCy Mastering NLP with spaCy: Part 4 The post Learn How to Use Transformers with HuggingFace and SpaCy appeared first on Towards Data Science. Marcello Politi Go to original source

  • What is Universality in LLMs? How to Find Universal Neurons

    What is Universality in LLMs? How to Find Universal Neurons How independently trained transformers form same the neurons The post What is Universality in LLMs? How to Find Universal Neurons appeared first on Towards Data Science. Shuyang Go to original source

  • A Brief History of GPT Through Papers

    A Brief History of GPT Through Papers Language models are becoming really good. But where did they come from? The post A Brief History of GPT Through Papers appeared first on Towards Data Science. Rohit Pandey Go to original source

  • Positional Embeddings in Transformers: A Math Guide to RoPE & ALiBi

    Positional Embeddings in Transformers: A Math Guide to RoPE & ALiBi Learn APE, RoPE, and ALiBi positional embeddings for GPT — intuitions, math, PyTorch code, and experiments on TinyStories The post Positional Embeddings in Transformers: A Math Guide to RoPE & ALiBi appeared first on Towards Data Science. Sathya Krishnan Suresh Go to original source

  • Circuit Tracing: A Step Closer to Understanding Large Language Models

    Circuit Tracing: A Step Closer to Understanding Large Language Models Context Over the years, Transformer-based large language models (LLMs) have made substantial progress across a wide range of tasks evolving from simple information retrieval systems to sophisticated agents capable of coding, writing, conducting research, and much more. But despite their capabilities, these models are still largely…

  • Image Captioning, Transformer Mode On

    Image Captioning, Transformer Mode On Introduction In my previous article, I discussed one of the earliest Deep Learning approaches for image captioning. If you’re interested in reading it, you can find the link to that article at the end of this one. Today, I would like to talk about Image Captioning again, but this time…