A machine learning framework with readable source code.
haifeng-jin/readable-ml-framework की मुख्य विशेषताएं हैं: Deep Learning Frameworks, Frameworks and Implementations।
haifeng-jin/readable-ml-framework के ओपन-सोर्स विकल्पों में शामिल हैं: hpcaitech/colossalai — ColossalAI is a distributed deep learning framework designed for training and deploying massive artificial… albu/albumentations — Albumentations is an image augmentation library and computer vision preprocessing tool designed to expand datasets for… alrojo/tensorflow-tutorial — Practical tutorials and labs for TensorFlow used by Nvidia, FFN, CNN, RNN, Kaggle, AE. amznlabs/amazon-dsstne — Amazon DSSTNE is a machine learning toolkit and sparse tensor network library designed for deep learning models with… andersbll/deeppy — Deep learning in Python. alankbi/detecto — Build fully-functioning computer vision models with PyTorch.
ColossalAI is a distributed deep learning framework designed for training and deploying massive artificial intelligence models across clusters of hardware accelerators. It functions as a parallel computing engine that partitions model workloads and data across multiple processors to maximize memory efficiency and throughput. The platform distinguishes itself through a comprehensive suite of parallelization strategies, including multi-dimensional tensor parallelism and pipeline-based model parallelism, which segment neural network layers and stages across devices. To support large-scale genera
Albumentations is an image augmentation library and computer vision preprocessing tool designed to expand datasets for deep learning models. It provides a collection of transformations that modify pixel values and spatial geometry to increase the diversity of training samples and improve model generalization. The library supports both 2D image augmentation and 3D volumetric data augmentation. It handles a variety of labels alongside images, ensuring that bounding boxes, keypoints, and segmentation masks remain accurately aligned when spatial transformations are applied. The tool incorporates
Practical tutorials and labs for TensorFlow used by Nvidia, FFN, CNN, RNN, Kaggle, AE
