Deep learning in Python
Les fonctionnalités principales de andersbll/deeppy sont : Deep Learning Frameworks, General Machine Learning.
Les alternatives open-source à andersbll/deeppy incluent : benedekrozemberczki/karateclub — Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (CIKM 2020). catalyst-team/catalyst — Accelerated deep learning R&D. amznlabs/amazon-dsstne — Amazon DSSTNE is a machine learning toolkit and sparse tensor network library designed for deep learning models with… apache/incubator-mxnet — Apache MXNet is a deep learning framework and distributed machine learning library designed for training and deploying… bvlc/caffe — Caffe is a high-performance deep learning framework designed for training and deploying deep neural networks. It… clementfarabet/lua---nnx — An extension to Torch7's nn package.
Apache MXNet is a deep learning framework and distributed machine learning library designed for training and deploying neural networks across distributed systems, mobile devices, and hardware accelerators. It functions as a cross-platform runtime and a dynamic dataflow scheduler that optimizes neural network execution. The framework provides a multi-language API, enabling the development of machine learning models using Python, R, Julia, Scala, Go, and JavaScript. It supports high-performance model training and the scaling of workloads across multiple GPUs and machines. The system covers cap
Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (CIKM 2020)
Amazon DSSTNE is a machine learning toolkit and sparse tensor network library designed for deep learning models with sparse inputs and outputs. It provides a model-parallel training framework and a GPU-accelerated sparse engine to support memory-intensive networks. The framework is specifically designed for recommendation system training and large-scale sparse learning. It enables the distribution of large weight matrices and embedding tables across multiple GPU devices to handle models that exceed the memory capacity of a single processor. The project covers a broad range of capabilities in
Caffe is a high-performance deep learning framework designed for training and deploying deep neural networks. It functions as a machine learning engine and a convolutional neural network library, providing a C++ backend to accelerate computations on both GPUs and CPUs. The system includes a specialized toolset for computer vision, enabling tasks such as object detection, semantic segmentation, and large-scale image retrieval. It supports the deployment of pre-trained models for image and scene recognition, as well as the ability to fine-tune neural network weights for specialized tasks. The