Mxnet
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A flexible and efficient deep learning framework that mixes symbolic and imperative programming for heterogeneous distributed systems.
A flexible and efficient deep learning framework that mixes symbolic and imperative programming for heterogeneous distributed systems.
A flexible and efficient deep learning framework that mixes symbolic and imperative programming for heterogeneous distributed systems.
A library that enables PyTorch, Chainer, MXNet, and NumPy users to write TensorBoard events with simple function calls.
A comprehensive toolset for converting, visualizing, and managing deep learning models across multiple frameworks like TensorFlow, PyTorch, and Caffe.
A complete AI-driven process using GANs with LSTM and CNN to predict stock price movements, incorporating diverse data sources and hyperparameter optimization.
An engine-agnostic deep learning framework for Java developers, providing a high-level API for model training and inference.
An open-source platform for building, training, and monitoring large-scale deep learning applications with full lifecycle MLOps.
A simple and versatile framework for object detection and instance recognition with extensive model coverage and distributed training.
A lightweight deep learning library with a functional API for composing models, compatible with PyTorch, TensorFlow, and MXNet.
A curated collection of open-source computer vision pre-trained models across TensorFlow, Keras, PyTorch, Caffe, and MXNet frameworks.
.NET Standard bindings for Apache MXNet, providing C# developers with NumPy-compatible APIs for machine learning model development, training, and deployment.
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