A GPU-accelerated library containing highly optimized building blocks and an execution engine for data processing to accelerate deep learning training and inference applications.
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Updated
Jun 11, 2024 - C++
A GPU-accelerated library containing highly optimized building blocks and an execution engine for data processing to accelerate deep learning training and inference applications.
AWS Deep Learning Containers (DLCs) are a set of Docker images for training and serving models in TensorFlow, TensorFlow 2, PyTorch, and MXNet.
ncnn is a high-performance neural network inference framework optimized for the mobile platform
AI on Hadoop
A library for training and deploying machine learning models on Amazon SageMaker
Open standard for machine learning interoperability
State-of-the-art 2D and 3D Face Analysis Project
TensorLy: Tensor Learning in Python.
Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.
An Engine-Agnostic Deep Learning Framework in Java
The Unified AI Framework
A high-performance ML model serving framework, offers dynamic batching and CPU/GPU pipelines to fully exploit your compute machine
🔮 A refreshing functional take on deep learning, compatible with your favorite libraries
The Java implementation of Dive into Deep Learning (D2L.ai)
Probabilistic time series modeling in Python
State-of-the-Art Deep Learning scripts organized by models - easy to train and deploy with reproducible accuracy and performance on enterprise-grade infrastructure.
MMdnn is a set of tools to help users inter-operate among different deep learning frameworks. E.g. model conversion and visualization. Convert models between Caffe, Keras, MXNet, Tensorflow, CNTK, PyTorch Onnx and CoreML.
Some Data Science examples using Groovy
Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.
MLOps Tools For Managing & Orchestrating The Machine Learning LifeCycle
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