An Engine-Agnostic Deep Learning Framework in Java
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Updated
May 12, 2024 - Java
An Engine-Agnostic Deep Learning Framework in Java
ncnn is a high-performance neural network inference framework optimized for the mobile platform
A high-performance ML model serving framework, offers dynamic batching and CPU/GPU pipelines to fully exploit your compute machine
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.
A library for training and deploying machine learning models on Amazon SageMaker
State-of-the-art 2D and 3D Face Analysis Project
Open standard for machine learning interoperability
Deep Learning Inference benchmark. Supports OpenVINO™ toolkit, Caffe, TensorFlow, TensorFlow Lite, ONNX Runtime, OpenCV DNN, MXNet, PyTorch, Apache TVM, ncnn, etc.
Probabilistic time series modeling in Python
Sandbox for training deep learning networks
A Deep Learning UCI-Chess Variant Engine written in C++ & Python 🦜
The Unified AI Framework
🔮 A refreshing functional take on deep learning, compatible with your favorite libraries
This project reproduces the book Dive Into Deep Learning (https://d2l.ai/), adapting the code from MXNet into PyTorch.
The Java implementation of Dive into Deep Learning (D2L.ai)
MLOps Tools For Managing & Orchestrating The Machine Learning LifeCycle
Learning Python A.I Framework
Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.
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