NikolayLutsyak

NikolayLutsyak's Stars

• mrpowers-io/tsumugi-spark

  SparkConnect Server plugin and protobuf messages for the Amazon Deequ Data Quality Engine.

  Language:Python276

• eugeneyan/ml-design-docs

  📝 Design doc template & examples for machine learning systems (requirements, methodology, implementation, etc.)

  55296

• joomcode/trace-analysis

  Library for performance bottleneck detection and optimization efficiency prediction

  Language:Scala362

• facebookexperimental/Robyn

  Robyn is an experimental, AI/ML-powered and open sourced Marketing Mix Modeling (MMM) package from Meta Marketing Science. Our mission is to democratise modeling knowledge, inspire the industry through innovation, reduce human bias in the modeling process & build a strong open source marketing science community.

  Language:Jupyter Notebook1.2k342

• karpathy/pytorch-made

  MADE (Masked Autoencoder Density Estimation) implementation in PyTorch

  Language:Python53985

• ikostrikov/pytorch-flows

  PyTorch implementations of algorithms for density estimation

  Language:Python57675

• kamenbliznashki/normalizing_flows

  Pytorch implementations of density estimation algorithms: BNAF, Glow, MAF, RealNVP, planar flows

  Language:Python605101

• rguo12/awesome-causality-algorithms

  An index of algorithms for learning causality with data

  2.9k448

• shenweichen/DeepCTR

  Easy-to-use,Modular and Extendible package of deep-learning based CTR models .

  Language:Python7.6k2.2k

• udibr/noisy_labels

  TRAINING DEEP NEURAL-NETWORKS USING A NOISE ADAPTATION LAYER

  Language:Jupyter Notebook11838

• bhanML/Masking

  NeurIPS'18: Masking: A New Perspective of Noisy Supervision

  Language:Python547

• bhanML/Co-teaching

  NeurIPS'18: Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels

  Language:Python491104

• iterative/dvc

  🦉 Data Versioning and ML Experiments

  Language:Python13.9k1.2k

• giorgiop/loss-correction

  Robust loss functions for deep neural networks (CVPR 2017)

  Language:Python8818

• GuokaiLiu/Noisy-Labels-Problem-Collection

  This is a collection of Papers and Codes for Noisy Labels Problem.

  Language:Jupyter Notebook6116

• facebookresearch/OctConv

  Code for paper

  Language:Python56380

• rlabbe/Kalman-and-Bayesian-Filters-in-Python

  Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filters, and more. All exercises include solutions.

  Language:Jupyter Notebook16.7k4.2k

• yulongwang12/visual-attribution

  Pytorch Implementation of recent visual attribution methods for model interpretability

  Language:Jupyter Notebook14526

• jindongwang/activityrecognition

  Resources about activity recognition-行为识别资料

  Language:MATLAB917352

• guillaume-chevalier/LSTM-Human-Activity-Recognition

  Human Activity Recognition example using TensorFlow on smartphone sensors dataset and an LSTM RNN. Classifying the type of movement amongst six activity categories - Guillaume Chevalier

  Language:Jupyter Notebook3.3k937