Unlimiformer: Long-Range Transformers with Unlimited Length Input
Author(s): Reza Yazdanfar Originally published on Towards AI. Now it’s possible to have deep learning models with no limitation for the input size. unsplash Attention-based transformers have revolutionized the AI industry since 2017. Since then, we have seen significant progress in all …
Exploring the Power of the Transformers Library for Natural Language Processing
Author(s): Rafay Qayyum Originally published on Towards AI. Natural language processing (NLP) is a branch of Artificial Intelligence that deals with giving computers the ability to understand text and spoken words in the same way human beings can.NLP has made significant advancements …
Build and Train Vision Transformer from Scratch
Author(s): Mikhail Kravets Originally published on Towards AI. Image by author A few years ago, it was hard to imagine what a transformer is; today, it is hard to imagine a modern neural network that doesn’t use transformers. In this tutorial, we’ll …
Taking a Walk in the OpenAI Gym: Using Decision Transformer to Power Reinforcement Learning
Author(s): Brent Larzalere Originally published on Towards AI. Perform Deep Reinforcement Learning using the Decision Transformer deepmind-lISkvdgfLEk-unsplash This article will describe how to use a decision transformer model to perform deep reinforcement learning in the OpenAI gym. PyTorch will be used …
Memorizing Transformer
Author(s): Reza Yazdanfar Originally published on Towards AI. How To Scale Transformers’ Memory up to 262K Tokens With a Minor Change?Extending Transformers by memorizing up to 262K tokens This article is a fabulous attempt to leverage language models in memorizing information by …
Unlocking New Insights with Vision Transformer
Author(s): Anay Dongre Originally published on Towards AI. Image generated by DALL.E 2 The Vision Transformer (ViT) is a state-of-the-art deep learning model designed to process and interpret visual information. It utilizes a novel attention-based approach to identify key features and patterns …
You Can No Longer Fail To Understand How To Use Large Language Models
Author(s): Michaël Karpe Originally published on Towards AI. A hands-on approach to learning how Large Language Models work in practice. Image by Alexandra Koch from Pixabay. Why a new article on Large Language Models? The launch and incredible speed of adoption of …
How To Scale Transformers’ Memory up to 262K Tokens With a Minor Change?
Author(s): Reza Yazdanfar Originally published on Towards AI. Extending Transformers by memorizing up to 262K tokens This member-only story is on us. Upgrade to access all of Medium. This article is a fabulous attempt to leverage language models in memorizing information by …
ChatGPT on Your Own Terms: Building Your Own Language Model
Author(s): Anay Dongre Originally published on Towards AI. This article showcases how you can build your own version of ChatGPT using PyTorch. We are not going to be able to reproduce the exact replica of chatGPT as it is a production-level system …
A Journey into the Fabulous Applications of Transformers — Part 2
Author(s): Dr. Dharini R Originally published on Towards AI. Demo with Emphasis on NLP using Python, Hugging Face Photo by Samule Sun on Unsplash Transformer architecture is widely used in Natural Language Processing and it highly contributed to the need-of-the-hour Large Language …
Overview Of Vision Transformers Is All You Need
Author(s): Mustafa Gültekin Originally published on Towards AI. How are Transformers the next breakthrough in computer vision? This member-only story is on us. Upgrade to access all of Medium. Photo by Joshua Earle on Unsplash In the history of transformers in deep …
Easy Object Detection with Transformers: Simple Implementation of Pix2Seq Model in PyTorch
Author(s): Moein Shariatnia Originally published on Towards AI. Top highlight My Simple Implementation of Pix2Seq U+007C Image by author Introduction Object detection does not have to be a difficult task! I clearly remember the first time I implemented YOLO from scratch, and …
Generative AI and Future
Author(s): Luhui Hu Originally published on Towards AI. GAN, GPT-3, DALL·E 2, and what’s next Photo by Josh Gordon on Unsplash The past ten years have been the golden decade of AI, but meaningful AI has just begun: CV is the current …
VQ-GAN & Transformer — Taming Transformers for High-Resolution Image Synthesis: Synopsis
Author(s): Rohan Wadhawan Originally published on Towards AI. Summary This post summarizes the work “Taming Transformers for High-Resolution Image Synthesis” by Patrick Esser, Robin Rombach, and Björn Ommer. It highlights the key take-home messages, the scope of improvement, and the applications of …
Understanding BERT
Author(s): Shweta Baranwal Originally published on Towards AI. Source: Photo by Min An on Pexels Natural Language Processing BERT (Bidirectional Encoder Representations from Transformers) is a research paper published by Google AI language. Unlike previous versions of NLP architectures, BERT is conceptually …
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