Jay alammar

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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….

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 jay alammar January 22nd. It then rapidly starts to power Google Search and Bing Search. Venom appears in the intro cinematic of Nemesis 2, a video game.

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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. The encoders are all identical in structure yet they do not share weights.

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No contributions on December 2nd. In it, we present explorables and visualizations aiding the intuition of:. No contributions on March 31st. No contributions on February 13th. No contributions on February 11th. No contributions on June 21st. No contributions on April 6th. No contributions on September 2nd. One of the easiest ways to think about that, is that you can load tables and excel files and then slice and dice them in multiple ways:. Stable Diffusion is versatile in that it can be used in a number of different ways. Your browser does not support the video tag.

Is it the future or the present?

By visualizing the hidden state between a model's layers, we can get some clues as to the model's "thought process". The following articles are merged in Scholar. Machine learning research communication via illustrated and interactive web articles J Alammar Beyond static papers: Rethinking how we share scientific understanding in ML … , In the back of my head was the idea that the entire field of Big Data and technologies like Hadoop were vastly accelerated when Google researchers released their Map Reduce paper. Skip to content. No contributions on March 24th. No contributions on September 5th. No contributions on January 22nd. No contributions on May 2nd. No contributions on October 7th. Title Sort Sort by citations Sort by year Sort by title. Tuesday Tue No contributions on February 28th. No contributions on December 16th.