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#

large-scale-machine-learning

Here are 24 public repositories matching this topic...

distributed-ml-patternsDeepFace

Keras implementation of the renowned publication "DeepFace: Closing the Gap to Human-Level Performance in Face Verification" by Taigman et al. Pre-trained weights on VGGFace2 dataset.

  • UpdatedJul 11, 2021
  • Python
Perceiver-Music-Transformer

A comprehensive guide designed to empower readers with advanced strategies and practical insights for developing, optimizing, and deploying scalable AI models in real-world applications.

  • UpdatedApr 12, 2024

Materials for "Machine Learning on Big Data" course

  • UpdatedJul 23, 2023
  • Jupyter Notebook

This is the official codebase for KDD 2021 paper Generalized Zero-Shot Extreme Multi-Label Learning

  • UpdatedJul 25, 2022
  • C++

A fully adaptive, zero-tuning parameter manager that enables efficient distributed machine learning training

  • UpdatedFeb 23, 2023
  • C++

Execution framework for multi-task model parallelism. Enables the training of arbitrarily large models with a single GPU, with linear speedups for multi-gpu multi-task execution.

  • UpdatedAug 13, 2023
  • Python
EuterpeGIGA-Piano

Keras implementation of the renowned publication "FaceNet: A Unified Embedding for Face Recognition and Clustering" by Schroff et al.

  • UpdatedMar 25, 2023
  • Python

Official Code Base for ICLR 2024 paper Enhancing Tail Performance in Extreme Classifiers by Label Variance Reduction

  • UpdatedMar 14, 2024
  • Python

This project is for developing a deep neural networks and its variant from scratch. No external libraries are used except for GPU operations.

  • UpdatedMay 11, 2021
  • Java

Our full length research is finally acknowledged through double-blind review procedure.

  • UpdatedJun 8, 2022

Focused on overlooked system design principles crucial for large-scale AI applications. This repository explores architecture, infrastructure, and integration, helping developers build scalable, efficient AI systems for real-world demands. It highlights key innovations and their impact on AI system design.

  • UpdatedAug 21, 2025

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