Jay alammar

The system can't perform the operation now. Try again later. Citations per year.

Prevent this user from interacting with your repositories and sending you notifications. Learn more about blocking users. Learn more about reporting abuse. Explain, analyze, and visualize NLP language models. Ecco creates interactive visualizations directly in Jupyter notebooks explaining the behavior of Transformer-based language models like GPT2, B….

Jay alammar

Is it the future or the present? Can AI Image generation tools make re-imagined, higher-resolution versions of old video game graphics? Over the last few days, I used AI image generation to reproduce one of my childhood nightmares. I wrestled with Stable Diffusion, Dall-E and Midjourney to see how these commercial AI generation tools can help retell an old visual story - the intro cinematic to an old video game Nemesis 2 on the MSX. This fine-looking gentleman is the villain in a video game. Venom appears in the intro cinematic of Nemesis 2, a video game. This image, in particular, comes at a dramatic reveal in the cinematic. This figure does not show the final Dr. Venom graphic because I want you to witness it as I had, in the proper context and alongside the appropriate music. You can watch that here:. Translations: Chinese , Vietnamese. V2 Nov : Updated images for more precise description of forward diffusion. A few more images in this version. The ability to create striking visuals from text descriptions has a magical quality to it and points clearly to a shift in how humans create art.

No contributions on November 11th.

.

Prevent this user from interacting with your repositories and sending you notifications. Learn more about blocking users. Learn more about reporting abuse. Explain, analyze, and visualize NLP language models. Ecco creates interactive visualizations directly in Jupyter notebooks explaining the behavior of Transformer-based language models like GPT2, B…. Jupyter Notebook 1. Build a Jekyll blog in minutes, without touching the command line. Jupyter Notebook

Jay alammar

In the previous post, we looked at Attention — a ubiquitous method in modern deep learning models. Attention is a concept that helped improve the performance of neural machine translation applications. In this post, we will look at The Transformer — a model that uses attention to boost the speed with which these models can be trained. The biggest benefit, however, comes from how The Transformer lends itself to parallelization. A TensorFlow implementation of it is available as a part of the Tensor2Tensor package. In this post, we will attempt to oversimplify things a bit and introduce the concepts one by one to hopefully make it easier to understand to people without in-depth knowledge of the subject matter. In a machine translation application, it would take a sentence in one language, and output its translation in another.

Table pads for dining table

The biggest benefit, however, comes from how The Transformer lends itself to parallelization. No contributions on June 23rd. This exposition series continues the pursuit to interpret and visualize the inner-workings of transformer-based language models. No contributions on November 20th. No contributions on February 11th. For a while, it seemed like scaling larger and larger models is the main way to improve performance. No contributions on February 10th. New articles related to this author's research. No contributions on May 27th. No contributions on August 2nd. Stable Diffusion is versatile in that it can be used in a number of different ways. All speakers have a software background.

Is it the future or the present? Can AI Image generation tools make re-imagined, higher-resolution versions of old video game graphics? Over the last few days, I used AI image generation to reproduce one of my childhood nightmares.

No contributions on September 15th. This article explains the model and not what is especially novel about it. No contributions on November 19th. No contributions on July 22nd. No contributions on July 28th. No contributions on April 4th. No contributions on May 31st. No contributions on August 31st. In this post, we will look at The Transformer — a model that uses attention to boost the speed with which these models can be trained. No contributions on November 24th. No contributions on November 17th. No contributions on January 13th. No contributions on August 24th.