Stars
Language
Sort by: Recently starred
Snowflake Guide: Building a Recommendation Engine Using Snowflake & Amazon SageMaker
A tool to allow batch PDF export for free Notion users. You can export as HTML and then use this tool to convert those into PDFs.
Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.
Distributed Machine Learning with Python, published by Packt
Example code for Fluent Python, 2nd edition (O'Reilly 2022)
A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.
Python Data Science Handbook: full text in Jupyter Notebooks
Transformer models from BERT to GPT-4, environments from Hugging Face to OpenAI. Fine-tuning, training, and prompt engineering examples. A bonus section with ChatGPT, GPT-3.5-turbo, GPT-4, and DALL…
This is an example of a Containerized Flask Application that can deploy to many target environments including: AWS, GCP and Azure.
ComposeTransformers: the end-to-end recommender system for woman clothing via image-text content retrieval.
FairBook: A Reproducibility Study on The Unfairness of Popularity Bias in Book Recommendation (Bias@ECIR 2022)
A Real-World Benchmark for Reinforcement Learning based Recommender System
Code Repository for Machine Learning with PyTorch and Scikit-Learn
Code and Results for the paper: A Revisiting Study of Appropriate Offline Evaluation for Top-𝑁 Recommendation Algorithms.
[Official Codes] Experiments on Generalizability of User-Oriented Fairness in Recommender Systems (SIGIR 2022)
Repository for Recommender System Challenge at Politecnico di Milano
Investigation of product recommendation approaches and open datasets
The python implementation of our "UA-FedRec: Untargeted Attack on Federated News Recommendation" in KDD 2023.
LibRerank is a toolkit for re-ranking algorithms. There are a number of re-ranking algorithms, such as PRM, DLCM, GSF, miDNN, SetRank, EGRerank, Seq2Slate.