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Awesome GitHub RepositoriesMachine Learning Workflows
End-to-end processes and sequences of operations required to build, train, and evaluate machine learning models.
Explore 4 awesome GitHub repositories matching artificial intelligence & ml · Machine Learning Workflows. Refine with filters or upvote what's useful.
Awesome Machine Learning Workflows GitHub Repositories
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- tensorflow/models
This repository serves as a centralized collection of state-of-the-art deep learning architectures and reference implementations designed for research and application development. It provides a comprehensive toolkit for computer vision and natural language processing, offering pre-built models and training pipelines fo
Python - keras-team/keras
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
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 - RVC-Boss/GPT-SoVITS
GPT-SoVITS is a text-to-speech synthesis engine and voice cloning toolkit designed for generating natural-sounding human speech. It functions as a neural audio processing pipeline that maps input text to high-fidelity audio waveforms, utilizing conditional variational autoencoders and flow-based decoders to ensure expr
Pythontext-to-speechttsvits
Explore sub-tags
- Custom Vision TrainingTools and methods for fine-tuning computer vision models on specialized or proprietary datasets.
- Dataset ConfigurationsStandardized file formats used to define data paths, class labels, and training/validation splits for machine learning models.
- Deep Learning Best PracticesStandardized patterns for model architecture, training loops, and data pipelines to ensure reproducibility.
- Model Fine-Tuning