Trax — Deep Learning with Clear Code and Speed

Transformer models from BERT to GPT-4, environments from Hugging Face to OpenAI. Fine-tuning, training, and prompt engineering examples. A bonus section with ChatGPT, GPT-3.5-turbo, GPT-4, and DALL-E including jump starting GPT-4, speech-to-text, text-to-speech, text to image generation with DALL-E, Google Cloud AI,HuggingGPT, and more

Transformers 3rd Edition

EyeTrax – webcam-based eye tracking made simple

Osint tool for track ip adress

The official VOT Challenge evaluation and analysis toolkit

traxss | Automated XSS Vulnerability Scanner Currently In Development :snake: HACKTOBERFEST PROJECT 2019

Tracking tool for Path of Exile

Reference implementation of the Visual Tracking eXchange protocol.

A Generative Toolkit for NestJS Developers. Harness the speed of a BaaS while preserving complete freedom and control over your NestJS stack.

Simple time tracker designed for ChatOps

Deep learning-driven multi animal tracking and pose estimation add-on for Blender

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Python library that implements connecting and communicating with the GraviTrax POWER Element Connect via BLE. It includes a GUI and console sample application that allows controlling the POWER RF stones via the GravitraxConnect.

🚀 Geo-trax is a comprehensive pipeline for extracting and analyzing high-accuracy georeferenced vehicle trajectories from quasi-stationary, bird’s-eye view drone footage. Using advanced computer vision and deep learning, it enables detailed urban traffic analysis and supports scalable, precise studies of vehicle dynamics.

Pokemon TCG collection management

Sentiment analysis for Twitter's tweet (in Indonesia language) was built with 3 models to get a comparison in determining which model gives the best results for predicting a tweet to have a positive or negative meaning.

PHP on Trax - Official Repository

An abstractive text summarization model based on Transformer Decoder (GPT-2) using Google/Trax.

Colab notebooks exploring different Machine Learning topics.

#Assignment Answers #About this Specialization: Natural Language Processing (NLP) uses algorithms to understand and manipulate human language. This technology is one of the most broadly applied areas of machine learning. As AI continues to expand, so will the demand for professionals skilled at building models that analyze speech and language, uncover contextual patterns, and produce insights from text and audio. By the end of this Specialization, you will be ready to design NLP applications that perform question-answering and sentiment analysis, create tools to translate languages and summarize text, and even build chatbots. These and other NLP applications are going to be at the forefront of the coming transformation to an AI-powered future. This Specialization is designed and taught by two experts in NLP, machine learning, and deep learning. Younes Bensouda Mourri is an Instructor of AI at Stanford University who also helped build the Deep Learning Specialization. Łukasz Kaiser is a Staff Research Scientist at Google Brain and the co-author of Tensorflow, the Tensor2Tensor and Trax libraries, and the Transformer paper.

macOS Music management application written in Flutter.

Coursera Natural Language Procession Specialization

tRNA Analysis of eXpression

Everything about the Traxmachine feature on Habbo Hotel

A puzzle game for the Amstrad CPC

An electron app for agile management of Github tasks with timers and reports - http://trax.management/

Catch OCaml exceptions and reraise them later without losing the original stack trace.

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This plugin transforms Moodle logs into xAPI statements, and sends then to your LRS.