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Greetings๐Ÿ‘‹ ! I'm Thien Dang - ThomaDs for short


๐Ÿ“Œ Currently, I'm diving into Diffusion models, particularly focusing on Colorization using Diffusion, aiming for first hand paper
๐Ÿ“Œ I'm also exploring the latest trends in Large Language Models (LLMs) including RAG++, Agents, Synthetic Data and other stuffs related to deployment
๐Ÿ’“ Glad to connect if you feel our collab would be a great match.

tnt305

๐ŸŒ Socials:

LinkedIn

๐Ÿ’ป Tech Stack:

PyTorch Python R LaTeX MySQL MongoDB MicrosoftSQLServer Pandas NumPy Matplotlib scikit-learn Scipy Power Bi Docker

๐Ÿ“Š GitHub Stats:

๐Ÿ† GitHub Trophies

โœ๏ธ What do i think?

**I'm fking hate this job. But I can't quit - I'm addicted !!!**

Thien Dang 's Projects

ai-recommendersystem icon ai-recommendersystem

่ฏฅไป“ๅบ“ๅฐ่ฏ•ๆ•ด็†ๆŽจ่็ณป็ปŸ้ข†ๅŸŸ็š„ไธ€ไบ›็ปๅ…ธ็ฎ—ๆณ•ๆจกๅž‹

bertweet icon bertweet

BERTweet: A pre-trained language model for English Tweets (EMNLP-2020)

crslab icon crslab

CRSLab is an open-source toolkit for building Conversational Recommender System (CRS).

ddpm-torch icon ddpm-torch

Unofficial PyTorch Implementation of Denoising Diffusion Probabilistic Models (DDPM)

df-gan icon df-gan

A Simple and Effective Baseline for Text-to-Image Synthesis (CVPR2022 oral)

hands-on-llms icon hands-on-llms

๐Ÿฆ– ๐—Ÿ๐—ฒ๐—ฎ๐—ฟ๐—ป about ๐—Ÿ๐—Ÿ๐— ๐˜€, ๐—Ÿ๐—Ÿ๐— ๐—ข๐—ฝ๐˜€, and ๐˜ƒ๐—ฒ๐—ฐ๐˜๐—ผ๐—ฟ ๐——๐—•๐˜€ for free by designing, training, and deploying a real-time financial advisor LLM system ~ ๐˜ด๐˜ฐ๐˜ถ๐˜ณ๐˜ค๐˜ฆ ๐˜ค๐˜ฐ๐˜ฅ๐˜ฆ + ๐˜ท๐˜ช๐˜ฅ๐˜ฆ๐˜ฐ & ๐˜ณ๐˜ฆ๐˜ข๐˜ฅ๐˜ช๐˜ฏ๐˜จ ๐˜ฎ๐˜ข๐˜ต๐˜ฆ๐˜ณ๐˜ช๐˜ข๐˜ญ๐˜ด

medmcqa icon medmcqa

A large-scale (194k), Multiple-Choice Question Answering (MCQA) dataset designed to address realworld medical entrance exam questions.

nemo icon nemo

NeMo: a toolkit for conversational AI

recbole-cdr icon recbole-cdr

This is a library built upon RecBole for cross-domain recommendation algorithms

text-to-image-diffusion icon text-to-image-diffusion

Experimental (working!) custom implementation of conditional and unconditional diffusion for testing new methods.

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