Showing 1-15 of 15 projects
Recommenders is a project for prototyping and operationalizing recommendation systems with Jupyter notebooks and best practices.
OpenVINO is an open-source toolkit for optimizing and deploying AI inference on a variety of hardware.
A comprehensive collection of study materials for deep learning interviews, covering various topics like machine learning, computer vision, and NLP.
Solutions and notes for the Machine Learning Specialization by Andrew NG on Coursera.
A unified, comprehensive and efficient recommendation library for building recommendation systems.
A PyTorch domain library for building recommendation systems using GPU-accelerated deep learning.
A comprehensive collection of research on knowledge graphs, covering various applications and techniques.
A TensorFlow-based recommendation algorithm and framework for building recommendation systems in Python.
An open-source toolkit for deep learning-based recommendation with TensorFlow.
NVTabular is a feature engineering and preprocessing library for tabular data used in recommender systems.
A library of recommendation algorithms based on Graph Neural Networks for information retrieval and recommendation systems.
A high-performance GPU framework for click-through-rate (CTR) estimation training in recommender systems.
A collection of must-read papers on recommendation systems and CTR prediction for developers working in computational advertising and AI-driven applications.
A comparative framework for building multimodal recommender systems using collaborative filtering and matrix factorization.
A collection of AI-related tutorials and resources for developers, focused on topics like machine learning, NLP, and data science.
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