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30-days-of-databricks

30 days of Databricks is a step-by-step guide to learn Databricks in 30 days for complete beginners.

applied-ml

📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production.

attention

visualizing attention for LLM users

azure-search-openai-demo

A sample app for the Retrieval-Augmented Generation pattern running in Azure, using Azure Cognitive Search for retrieval and Azure OpenAI large language models to power ChatGPT-style and Q&A experiences.

bert

TensorFlow code and pre-trained models for BERT

bertviz

BertViz: Visualize Attention in NLP Models (BERT, GPT2, BART, etc.)

black

The uncompromising Python code formatter

candle

Minimalist ML framework for Rust

classification-benchmarks

Thai text classification benchmarks

clikafkaintrowithadshane

CLI Special Live: Kafka Intro with Ad Shane

companionllm

CompanionLLM - A framework to finetune LLMs to be your own sentient conversational companion

cvat

Annotate better with CVAT, the industry-leading data engine for machine learning. Used and trusted by teams at any scale, for data of any scale.

data

data-challenge-kbtg

Data prediction challenges for Techjam Online Audition by KBTG. Finished in top 25.

data-science-tutorials

databricks-r2ae-dbc

datasciencewithpython

This contains python notebooks for Data Analysis

dbt-r2ae-demo-reference

dbt_r2ae_demo

deep-learning-keras-tf-tutorial

Learn deep learning with tensorflow2.0, keras and python through this comprehensive deep learning tutorial series. Learn deep learning from scratch. Deep learning series for beginners. Tensorflow tutorials, tensorflow 2.0 tutorial. deep learning tutorial python.

deep-learning-with-tensorflow-2

desktop

detectron2

Detectron2 is a platform for object detection, segmentation and other visual recognition tasks.

dgcnn

diffusers

🤗 Diffusers: State-of-the-art diffusion models for image and audio generation in PyTorch

docker-workshop

exhook

finnlp-sentiment

float16-example

ganhacks

starter from "How to Train a GAN?" at NIPS2016