Jay alammar

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The system can't perform the operation now. Try again later. Citations per year. Duplicate citations. The following articles are merged in Scholar. Their combined citations are counted only for the first article.

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.

By visualizing the hidden state between a model's layers, we can get some jay alammar as to the model's "thought process". No contributions on January 7th. Explain, analyze, and visualize NLP language models.

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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.

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. Popping open that Optimus Prime goodness, we see an encoding component, a decoding component, and connections between them. The decoding component is a stack of decoders of the same number.

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I experiment with PyNNDescent as well. No contributions on February 7th. October Oct. All speakers have a software background. No contributions on October 28th. No contributions on November 14th. No contributions on June 13th. No contributions on April 13th. Add an optional note: Note. No contributions on June 28th. No contributions on September 28th. Verified email at pegg.

Is it the future or the present?

After experimenting with AI image generation, you may start to wonder how it works. No contributions on December 13th. No contributions on July 15th. No contributions on March 12th. October Oct. May 25th update: New graphics RNN animation, word embedding graph , color coding, elaborated on the final attention example. Friday Fri No contributions on March 3rd. Venom graphic because I want you to witness it as I had, in the proper context and alongside the appropriate music. September Sep. Build a Jekyll blog in minutes, without touching the command line. No contributions on August 13th.