KristinaLifirenko's Stars
tensorflow/tensorflow
An Open Source Machine Learning Framework for Everyone
apache/superset
Apache Superset is a Data Visualization and Data Exploration Platform
recommenders-team/recommenders
Best Practices on Recommendation Systems
rapidsai/cudf
cuDF - GPU DataFrame Library
mbadry1/Top-Deep-Learning
Top 200 deep learning Github repositories sorted by the number of stars.
benhamner/Metrics
Machine learning evaluation metrics, implemented in Python, R, Haskell, and MATLAB / Octave
wangyongjie-ntu/Awesome-explainable-AI
A collection of research materials on explainable AI/ML
amaiya/ktrain
ktrain is a Python library that makes deep learning and AI more accessible and easier to apply
natasha/natasha
Solves basic Russian NLP tasks, API for lower level Natasha projects
evfro/polara
Recommender system and evaluation framework for top-n recommendations tasks that respects polarity of feedbacks. Fast, flexible and easy to use. Written in python, boosted by scientific python stack.
EngineerSpock/postgres-course-ru
jrzaurin/RecoTour
A tour through recommendation algorithms in python [IN PROGRESS]
piyushpathak03/Recommendation-systems
Recommendation Systems This is a workshop on using Machine Learning and Deep Learning Techniques to build Recommendation Systesm Theory: ML & DL Formulation, Prediction vs. Ranking, Similiarity, Biased vs. Unbiased Paradigms: Content-based, Collaborative filtering, Knowledge-based, Hybrid and Ensembles Data: Tabular, Images, Text (Sequences) Models: (Deep) Matrix Factorisation, Auto-Encoders, Wide & Deep, Rank-Learning, Sequence Modelling Methods: Explicit vs. implicit feedback, User-Item matrix, Embeddings, Convolution, Recurrent, Domain Signals: location, time, context, social, Process: Setup, Encode & Embed, Design, Train & Select, Serve & Scale, Measure, Test & Improve Tools: python-data-stack: numpy, pandas, scikit-learn, keras, spacy, implicit, lightfm Notes & Slides Basics: Deep Learning AI Conference 2019: WhiteBoard Notes | In-Class Notebooks Notebooks Movies - Movielens 01-Acquire 02-Augment 03-Refine 04-Transform 05-Evaluation 06-Model-Baseline 07-Feature-extractor 08-Model-Matrix-Factorization 09-Model-Matrix-Factorization-with-Bias 10-Model-MF-NNMF 11-Model-Deep-Matrix-Factorization 12-Model-Neural-Collaborative-Filtering 13-Model-Implicit-Matrix-Factorization 14-Features-Image 15-Features-NLP Ecommerce - YooChoose 01-Data-Preparation 02-Models News - Hackernews Product - Groceries Python Libraries Deep Recommender Libraries Tensorrec - Built on Tensorflow Spotlight - Built on PyTorch TFranking - Built on TensorFlow (Learning to Rank) Matrix Factorisation Based Libraries Implicit - Implicit Matrix Factorisation QMF - Implicit Matrix Factorisation Lightfm - For Hybrid Recommedations Surprise - Scikit-learn type api for traditional alogrithms Similarity Search Libraries Annoy - Approximate Nearest Neighbour NMSLib - kNN methods FAISS - Similarity search and clustering Learning Resources Reference Slides Deep Learning in RecSys by Balázs Hidasi Lessons from Industry RecSys by Xavier Amatriain Architecting Recommendation Systems by James Kirk Recommendation Systems Overview by Raimon and Basilico Benchmarks MovieLens Benchmarks for Traditional Setup Microsoft Tutorial on Recommendation System at KDD 2019 Algorithms & Approaches Collaborative Filtering for Implicit Feedback Datasets Bayesian Personalised Ranking for Implicit Data Logistic Matrix Factorisation Neural Network Matrix Factorisation Neural Collaborative Filtering Variational Autoencoders for Collaborative Filtering Evaluations Evaluating Recommendation Systems
pigidser/recommender_system
pigidser/Territory-Prediction
This is the final project for Specialization of Data Science course from SkillFactory company
bendith/skillfactory_rds_module_2_EDA
Skillfactory RealDataScience project #2