lhduc94

lhduc94's Stars

• dslp/dslp

  The Data Science Lifecycle Process is a process for taking data science teams from Idea to Value repeatedly and sustainably. The process is documented in this repo.

  46970

• great-expectations/great_expectations

  Always know what to expect from your data.

  Language:Python9.8k1.5k

• allegroai/clearml

  ClearML - Auto-Magical CI/CD to streamline your AI workload. Experiment Management, Data Management, Pipeline, Orchestration, Scheduling & Serving in one MLOps/LLMOps solution

  Language:Python5.6k644

• replicate/cog

  Containers for machine learning

  Language:Python7.8k545

• KindXiaoming/pykan

  Kolmogorov Arnold Networks

  Language:Jupyter Notebook14.6k1.3k

• Trusted-AI/AIF360

  A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitigate bias in datasets and models.

  Language:Python2.4k825

• stanfordnlp/dspy

  DSPy: The framework for programming—not prompting—foundation models

  Language:Python16.8k1.3k

• lhduc94/datasciencedances

  Language:TypeScript1

• MaartenGr/BERTopic

  Leveraging BERT and c-TF-IDF to create easily interpretable topics.

  Language:Python6k752

• chriskyfung/amp-affiliately-jekyll-theme

  An AMP-Ready Jekyll Blog Theme

  Language:SCSS1811

• rguo12/awesome-causality-algorithms

  An index of algorithms for learning causality with data

  2.9k441

• MaartenGr/KeyBERT

  Minimal keyword extraction with BERT

  Language:Python3.4k343

• apple/ml-fastvit

  This repository contains the official implementation of the research paper, "FastViT: A Fast Hybrid Vision Transformer using Structural Reparameterization" ICCV 2023

  Language:Python1.8k103

• CalculatedContent/WeightWatcher

  The WeightWatcher tool for predicting the accuracy of Deep Neural Networks

  Language:Python1.4k124

• evidentlyai/evidently

  Evidently is ​​an open-source ML and LLM observability framework. Evaluate, test, and monitor any AI-powered system or data pipeline. From tabular data to Gen AI. 100+ metrics.

  Language:Jupyter Notebook5.1k574

• rapidsai/deeplearning

  Language:Jupyter Notebook33690

• Yimeng-Zhang/feature-engineering-and-feature-selection

  A Guide for Feature Engineering and Feature Selection, with implementations and examples in Python.

  Language:Jupyter Notebook1.4k410

• ray-project/ray

  Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.

  Language:Python33.1k5.6k

• sinanuozdemir/feature_engineering_bookcamp

  Language:Jupyter Notebook5418

• julianfaraway/rexamples

  worked R examples

  Language:Stan11420

• yandex-research/tab-ddpm

  [ICML 2023] The official implementation of the paper "TabDDPM: Modelling Tabular Data with Diffusion Models"

  Language:Python37181

• Alibaba-NLP/HiAGM

  Hierarchy-Aware Global Model for Hierarchical Text Classification

  Language:Python20744

• Liam-E2/RotationForest

  A python implementation of the Rotation Forest algorithm per https://arxiv.org/abs/1809.06705.

  Language:Python209

• uber/causalml

  Uplift modeling and causal inference with machine learning algorithms

  Language:Python5k768

• CompVis/stable-diffusion

  A latent text-to-image diffusion model

  Language:Jupyter Notebook67.5k10.1k

• Nixtla/statsforecast

  Lightning ⚡️ fast forecasting with statistical and econometric models.

  Language:Python3.9k268

• VietHoang1512/Vietnamese-Spell-Correction

  Training and evaluating phase

  Language:Jupyter Notebook133

• keon/awesome-nlp

  :book: A curated list of resources dedicated to Natural Language Processing (NLP)

  16.5k2.6k

• google/trax

  Trax — Deep Learning with Clear Code and Speed

  Language:Python8k814

• pbcquoc/vietocr

  Transformer OCR

  Language:Jupyter Notebook594204