AWS Deep Learning Containers (DLCs) are a set of Docker images for training and serving models in TensorFlow, TensorFlow 2, PyTorch, and MXNet.
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Updated
Jul 8, 2024 - Python
AWS Deep Learning Containers (DLCs) are a set of Docker images for training and serving models in TensorFlow, TensorFlow 2, PyTorch, and MXNet.
An Engine-Agnostic Deep Learning Framework in Java
A GPU-accelerated library containing highly optimized building blocks and an execution engine for data processing to accelerate deep learning training and inference applications.
A library for training and deploying machine learning models on Amazon SageMaker
Open standard for machine learning interoperability
The Unified ML Representation
ncnn is a high-performance neural network inference framework optimized for the mobile platform
Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.
A high-performance ML model serving framework, offers dynamic batching and CPU/GPU pipelines to fully exploit your compute machine
State-of-the-art 2D and 3D Face Analysis Project
Simple Documentation Builder for Ivy Projects.
🔮 A refreshing functional take on deep learning, compatible with your favorite libraries
TensorLy: Tensor Learning in Python.
Sandbox for training deep learning networks
The Java implementation of Dive into Deep Learning (D2L.ai)
AI on Hadoop
Some Data Science examples using Groovy
Amazon SageMaker Managed Spot Training Examples
Deep Learning Inference benchmark. Supports OpenVINO™ toolkit, Caffe, TensorFlow, TensorFlow Lite, ONNX Runtime, OpenCV DNN, MXNet, PyTorch, Apache TVM, ncnn, etc.
适用于复杂场景的人脸识别身份认证系统
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