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Machine Learning Training Utilities · Awesome GitHub Repositories

3 repos

Awesome GitHub RepositoriesMachine Learning Training Utilities

Tools and techniques for managing, monitoring, and configuring the internal parameters and processes of model training.

Explore 3 awesome GitHub repositories matching artificial intelligence & ml · Machine Learning Training Utilities. Refine with filters or upvote what's useful.

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  • ultralytics/yolov5

    ultralytics/yolov5

    56,830GitHubView on GitHub↗

    YOLOv5 is a comprehensive computer vision framework designed for end-to-end deep learning, specializing in real-time object detection, image classification, and instance segmentation. It provides a unified toolkit that manages the entire lifecycle of a model, from initial dataset configuration and hyperparameter tuning

    Pythoncoremldeep-learningios
  • deepfakes/faceswap

    deepfakes/faceswap

    54,974GitHubView on GitHub↗

    Faceswap is a comprehensive framework for automated media manipulation and neural face synthesis. It provides a modular pipeline that manages the entire lifecycle of facial feature extraction, deep learning model training, and image conversion. By coordinating complex computer vision workflows, the system enables users

    Pythondeep-face-swapdeep-learningdeep-neural-networks
  • unslothai/unsloth

    unslothai/unsloth

    52,461GitHubView on GitHub↗

    Unsloth is a high-performance training and inference platform designed to optimize the lifecycle of large language and multimodal models. It provides a comprehensive engine for fine-tuning, executing, and managing models locally, with a focus on reducing memory consumption and increasing compute speed on consumer-grade

    Pythonagentdeepseekdeepseek-r1

Explore sub-tags

  • Fitness FunctionsWeighted metrics used to evaluate and guide the optimization of model performance during training.
  • Gradient Optimization TechniquesMethods for adjusting model gradients during training to improve stability and convergence.
  • Hyperparameter ConfigurationsFiles and settings that define training variables such as learning rates, loss gains, and augmentation strategies.
Layer Freezing
Techniques for disabling weight updates in specific neural network layers during training to optimize performance or prevent overfitting.
  • Model Weight ValidatorsTools that inspect model parameters for numerical stability and file integrity.
  • Training Progress MonitoringSystems for tracking metrics such as loss, gradient norms, and hardware utilization during model training.