tomwalczak's Stars
markhennings/google-sheets-ai
Make LLM calls from Google Sheets
AllYourBot/hostedgpt
An open version of ChatGPT you can host anywhere or run locally.
steven2358/awesome-generative-ai
A curated list of modern Generative Artificial Intelligence projects and services
run-house/runhouse
Dispatch and distribute your ML training to "serverless" clusters in Python, like PyTorch for ML infra. Iterable, debuggable, multi-cloud/on-prem, identical across research and production.
dair-ai/Prompt-Engineering-Guide
🐙 Guides, papers, lecture, notebooks and resources for prompt engineering
jagilley/fact-checker
Fact-checking LLM outputs with self-ask
langchain-ai/chat-langchain
gkamradt/langchain-tutorials
Overview and tutorial of the LangChain Library
karpathy/nanoGPT
The simplest, fastest repository for training/finetuning medium-sized GPTs.
aladdinpersson/Machine-Learning-Collection
A resource for learning about Machine learning & Deep Learning
sebastian-hofstaetter/teaching
Open-Source Information Retrieval Courses @ TU Wien
lppier/Topic_Modelling_Top2Vec_BERTopic
ddangelov/Top2Vec
Top2Vec learns jointly embedded topic, document and word vectors.
eugeneyan/applied-ml
📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production.
docker/awesome-compose
Awesome Docker Compose samples
markdouthwaite/streamlit-project
This repository provides a simple deployment-ready project layout for a Streamlit app. Simply swap out the code in `app.py` for your own and hit deploy!
chiphuyen/ml-interviews-book
https://huyenchip.com/ml-interviews-book/
sebastianruder/NLP-progress
Repository to track the progress in Natural Language Processing (NLP), including the datasets and the current state-of-the-art for the most common NLP tasks.
josephsdavid/N2D
Library implementation of https://arxiv.org/abs/1908.05968v5
sshkhr/BERTdeploy
A simple example of deploying a pre-trained BERT model as a REST API
MilaNLProc/contextualized-topic-models
A python package to run contextualized topic modeling. CTMs combine contextualized embeddings (e.g., BERT) with topic models to get coherent topics. Published at EACL and ACL 2021 (Bianchi et al.).
graykode/nlp-tutorial
Natural Language Processing Tutorial for Deep Learning Researchers
jwasham/coding-interview-university
A complete computer science study plan to become a software engineer.
apugoneappu/ask_me_anything
An easy-to-use app to visualise attentions of various VQA models.