deep-learning
OpenVINO™ is an open-source toolkit for optimizing and deploying AI inference.
OpenVINO is an open-source toolkit for optimizing and deploying deep learning models from cloud to edge. It accelerates deep learning inference across various use cases, such as generative AI, video, audio, and language with models from popular frameworks like PyTorch, TensorFlow, ONNX, and more. Convert and optimize models, and deploy across a mix of Intel® hardware and environments, on-premises and on-device, in the browser or in the cloud.
deep learning at the speed of light.
Luminal is a deep learning library that uses composable compilers to achieve high performance.
Triton is a language and compiler for parallel programming. It aims to provide a Python-based programming environment for productively writing custom DNN compute kernels capable of running at maximal throughput on modern GPU hardware.
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The world's simplest facial recognition api for Python and the command line.
Recognize and manipulate faces from Python or from the command line with the world's simplest face recognition library.
Coqui STT (frogSTT) is a fast, open-source, multi-platform, deep-learning toolkit for training and deploying speech-to-text models. frogSTT is battle tested in both production and research rocket
A hyperparameter optimization framework. Optimize Your Optimization.
An open source hyperparameter optimization framework to automate hyperparameter search
fastai is a deep learning library which provides practitioners with high-level components that can quickly and easily provide state-of-the-art results in standard deep learning domains, and provides researchers with low-level components that can be mixed and matched to build new approaches. It aims to do both things without substantial compromises in ease of use, flexibility, or performance. This is possible thanks to a carefully layered architecture, which expresses common underlying patterns of many deep learning and data processing techniques in terms of decoupled abstractions. These abstractions can be expressed concisely and clearly by leveraging the dynamism of the underlying Python language and the flexibility of the PyTorch library. fastai includes:
A free course designed for people with some coding experience, who want to learn how to apply deep learning and machine learning to practical problems.
Deep Learning Made Simple. Teach your app to see emotions. Build, train, and ship custom deep learning models using a simple visual interface.
Altify automizes the task of inserting alternative text attributes for image tags. Altify uses Microsoft Computer Vision API's deep learning algorithms to caption images in an HTML file and returns a new HTML file in which alt attributes are filled out with their corresponding captions.
Open Source Deep Learning for iOS, OS X and tvOS. DeepLearningKit is an Open Source – with Apache 2.0 Licence – Deep Learning Framework for Apple’s iOS, OS X and tvOS available at github.com/DeepLearningKit/DeepLearningKit.
PaddlePaddle (PArallel Distributed Deep LEarning) is an easy-to-use, efficient, flexible and scalable deep learning platform, which is originally developed by Baidu scientists and engineers for the purpose of applying deep learning to many products at Baidu.
CNTK (http://www.cntk.ai/), the Computational Network Toolkit by Microsoft Research, is a unified deep-learning toolkit that describes neural networks as a series of computational steps via a directed graph. In this directed graph, leaf nodes represent input values or network parameters, while other nodes represent matrix operations upon their inputs. CNTK allows to easily realize and combine popular model types such as feed-forward DNNs, convolutional nets (CNNs), and recurrent networks (RNNs/LSTMs). It implements stochastic gradient descent (SGD, error backpropagation) learning with automatic differentiation and parallelization across multiple GPUs and servers. CNTK has been available under an open-source license since April 2015. It is our hope that the community will take advantage of CNTK to share ideas more quickly through the exchange of open source working code.