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Machine Learning Training · Awesome GitHub Repositories

6 repos

Awesome GitHub RepositoriesMachine Learning Training

Frameworks and utilities used to train, fine-tune, and align machine learning models with specific objectives.

Explore 6 awesome GitHub repositories matching artificial intelligence & ml · Machine Learning Training. Refine with filters or upvote what's useful.

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Awesome Machine Learning Training GitHub Repositories

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  • openai/whisper

    openai/whisper

    94,839GitHubView on GitHub↗

    This project is a speech recognition and translation engine that utilizes a sequence-to-sequence transformer architecture to convert audio into text. It is built upon a weakly supervised learning framework, which leverages large-scale, unlabelled audio-transcript data to create generalized speech representations capabl

    Python
  • mlabonne/llm-course

    mlabonne/llm-course

    75,340GitHubView on GitHub↗

    This project is a comprehensive educational curriculum and engineering handbook focused on the lifecycle of large language models. It serves as a structured knowledge base for machine learning practitioners, covering the fundamental mathematical and architectural principles of transformer-based sequence modeling, as we

    courselarge-language-modelsllm
  • keras-team/keras

    keras-team/keras

    63,858GitHubView on GitHub↗

    Keras is a high-level deep learning framework designed for constructing and training neural networks through the composition of modular, functional layers. It serves as a comprehensive modeling toolkit that provides standardized procedures for defining, evaluating, and deploying complex architectures. By utilizing a di

    Pythondata-sciencedeep-learningjax
  • 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

  • Face Swapping ModelsSpecialized training pipelines for generating and refining face-replacement models.
  • Fine-Tuning Frameworks3 sub-tagsTools and methods for adapting pre-trained models to specific domains or tasks, distinct from general training by their focus on weight adjustment rather than initial model creation.
  • Fine-Tuning StrategiesTechniques for adapting pre-trained models to specific domains or tasks through supervised learning or preference alignment.
Machine Learning Training Utilities
6 sub-tags
Tools and techniques for managing, monitoring, and configuring the internal parameters and processes of model training.
  • Mathematical Training Objectives1 sub-tagCore mathematical components that define how a model learns, including loss functions, reward logic, and optimization strategies, distinct from infrastructure by focusing on the learning objective.
  • Model Tuning1 sub-tagSystems that automate the search for optimal model configurations by defining and exploring specific parameter search spaces.
  • Preference-Based Model AlignmentsTechniques for refining model behavior using human feedback or reward signals to optimize for safety and helpfulness.
  • Supervised Instruction Fine-TuningTechniques for adapting base models to specific task formats using curated input-output instruction datasets.
  • Training Acceleration Tools3 sub-tagsSoftware and hardware-level optimizations designed to increase throughput and reduce memory consumption, distinct from algorithmic logic by focusing on computational efficiency.
  • Training Configuration Management3 sub-tagsSystems for defining and managing the parameters, hyperparameters, and iteration schedules of a training run, distinct from execution engines by focusing on the setup phase.
  • Training Orchestration Systems3 sub-tagsPlatforms and utilities that manage the end-to-end lifecycle, execution, and monitoring of training jobs, distinct from specific algorithms by focusing on workflow management.
  • Weakly Supervised LearningTraining paradigms that utilize large-scale, noisy, or loosely paired datasets to improve model robustness.