frldj's Stars
graykode/nlp-tutorial
Natural Language Processing Tutorial for Deep Learning Researchers
geopandas/geopandas
Python tools for geographic data
Hironsan/bertsearch
Elasticsearch with BERT for advanced document search.
Agrover112/awesome-semantic-search
A curated list of awesome resources related to Semantic Search🔎 and Semantic Similarity tasks.
DataCanvasIO/HyperGBM
A full pipeline AutoML tool for tabular data
caiyinqiong/Semantic-Retrieval-Models
A curated list of awesome papers for Semantic Retrieval (TOIS Accepted: Semantic Models for the First-stage Retrieval: A Comprehensive Review).
aws-solutions-library-samples/fraud-detection-using-machine-learning
Setup end to end demo architecture for predicting fraud events with Machine Learning using Amazon SageMaker
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
VisionLearningGroup/Text-to-Clip_Retrieval
Implementation for "Multilevel Language and Vision Integration for Text-to-Clip Retrieval"
mraad/spark-pip
Spark job to perform massive Point in Polygon (PiP) operations
theamrzaki/COVID-19-BERT-ResearchPapers-Semantic-Search
BERT semantic search engine for searching literature research papers for coronavirus covid-19 in google colab
kavetinaveen/Deep-Learning-for-Semantic-Text-Matching
Deep Learning for Semantic Text Matching
rth/dl-lectures-labs
Slides and Jupyter notebooks for the Deep Learning lectures at M2 Data Science Université Paris Saclay
kbalog/uis-dat640-fall2020
Information Retrieval and Text Mining course at the University of Stavanger (DAT640), 2020 fall
bhagvank/NoCodeAIML
No Code AI/ML platform
jastfkjg/semantic-matching
semantic matching, text matching models
haandol/sagemaker-xgboost-pipeline-example
Ivan0131/SemMatch
文本匹配工具包, text matching, semantic matching
sohaib-ouzineb/Machine-Learning-Challenge
In this challenge, we are faced with the following task : given two faces and their similarity scores, tell whether these are the same faces or not with the best accuracy possible. We use different technics from Neural Networks, SVMs to ensemble methods like stacking, random forests and bagging.