Search Results for "neural network training"
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Showing 715 open source projects for "neural network training"
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Neural Network Intelligence
AutoML toolkit for automate machine learning lifecycle
Neural Network Intelligence is an open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning. NNI (Neural Network Intelligence) is a lightweight but powerful toolkit to help users automate feature engineering, neural architecture search, hyperparameter tuning and model compression. The tool manages automated machine learning (AutoML) experiments, dispatches and runs experiments... -
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Neural Tangents
Fast and Easy Infinite Neural Networks in Python
Neural Tangents is a high-level neural network API for specifying complex, hierarchical models at both finite and infinite width, built in Python on top of JAX and XLA. It lets researchers define architectures from familiar building blocks—convolutions, pooling, residual connections, and nonlinearities—and obtain not only the finite network but also the corresponding Gaussian Process (GP) kernel of its infinite-width limit. With a single specification, you can compute NNGP and NTK kernels... -
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Tiny CUDA Neural Networks
Lightning fast C++/CUDA neural network framework
This is a small, self-contained framework for training and querying neural networks. Most notably, it contains a lightning-fast "fully fused" multi-layer perceptron (technical paper), a versatile multiresolution hash encoding (technical paper), as well as support for various other input encodings, losses, and optimizers. We provide a sample application where an image function (x,y) -> (R,G,B) is learned. The fully fused MLP component of this framework requires a very large amount of shared... -
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Stock prediction deep neural learning
Predicting stock prices using a TensorFlow LSTM
Predicting stock prices can be a challenging task as it often does not follow any specific pattern. However, deep neural learning can be used to identify patterns through machine learning. One of the most effective techniques for series forecasting is using LSTM (long short-term memory) networks, which are a type of recurrent neural network (RNN) capable of remembering information over a long period of time. This makes them extremely useful for predicting stock prices. Predicting stock prices... -
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PyTorch
Open source machine learning framework
PyTorch is a Python package that offers Tensor computation (like NumPy) with strong GPU acceleration and deep neural networks built on tape-based autograd system. This project allows for fast, flexible experimentation and efficient production. PyTorch consists of torch (Tensor library), torch.autograd (tape-based automatic differentiation library), torch.jit (a compilation stack [TorchScript]), torch.nn (neural networks library), torch.multiprocessing (Python multiprocessing... -
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TensorRT
C++ library for high performance inference on NVIDIA GPUs
NVIDIA® TensorRT™ is an SDK for high-performance deep learning inference. It includes a deep learning inference optimizer and runtime that delivers low latency and high throughput for deep learning inference applications. TensorRT-based applications perform up to 40X faster than CPU-only platforms during inference. With TensorRT, you can optimize neural network models trained in all major frameworks, calibrate for lower precision with high accuracy, and deploy to hyperscale data centers... -
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Netron
Visualizer for neural network, deep learning, machine learning models
Netron is a viewer for neural network, deep learning and machine learning models. Netron supports ONNX, Keras, TensorFlow Lite, Caffe, Darknet, Core ML, MNN, MXNet, ncnn, PaddlePaddle, Caffe2, Barracuda, Tengine, TNN, RKNN, MindSpore Lite, and UFF. Netron has experimental support for TensorFlow, PyTorch, TorchScript, OpenVINO, Torch, Arm NN, BigDL, Chainer, CNTK, Deeplearning4j, MediaPipe, ML.NET, scikit-learn, TensorFlow.js. There is an extense variety of sample model files to download or open... -
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SentencePiece
Unsupervised text tokenizer for Neural Network-based text generation
SentencePiece is an unsupervised text tokenizer and detokenizer mainly for Neural Network-based text generation systems where the vocabulary size is predetermined prior to the neural model training. SentencePiece implements subword units (e.g., byte-pair-encoding (BPE) [Sennrich et al.]) and unigram language model [Kudo.]) with the extension of direct training from raw sentences. SentencePiece allows us to make a purely end-to-end system that does not depend on language-specific pre... -
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waifu2x ncnn Vulkan
waifu2x converter ncnn version, run fast GPU with vulkan
ncnn implementation of waifu2x converter. Runs fast on Intel/AMD/Nvidia/Apple-Silicon with Vulkan API. waifu2x-ncnn-vulkan uses ncnn project as the universal neural network inference framework. -
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ncnn
High-performance neural network inference framework for mobile
ncnn is a high-performance neural network inference computing framework designed specifically for mobile platforms. It brings artificial intelligence right at your fingertips with no third-party dependencies, and speeds faster than all other known open source frameworks for mobile phone cpu. ncnn allows developers to easily deploy deep learning algorithm models to the mobile platform and create intelligent APPs. It is cross-platform and supports most commonly used CNN networks, including... -
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AUTOMATIC1111 Stable Diffusion web UI
Stable Diffusion web UI
AUTOMATIC1111's stable-diffusion-webui is a powerful, user-friendly web interface built on the Gradio library that allows users to easily interact with Stable Diffusion models for AI-powered image generation. Supporting both text-to-image (txt2img) and image-to-image (img2img) generation, this open-source UI offers a rich feature set including inpainting, outpainting, attention control, and multiple advanced upscaling options. With a flexible installation process across Windows, Linux, and... -
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NeuralOperators.jl
DeepONets, Neural Operators, Physics-Informed Neural Ops in Julia
Neural operator is a novel deep learning architecture. It learns an operator, which is a mapping between infinite-dimensional function spaces. It can be used to resolve partial differential equations (PDE). Instead of solving by finite element method, a PDE problem can be resolved by training a neural network to learn an operator mapping from infinite-dimensional space (u, t) to infinite-dimensional space f(u, t). Neural operator learns a continuous function between two continuous function... -
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TensorFlow.js
TensorFlow.js is a library for machine learning in JavaScript
... to potentially higher dimensions. Built on top of TensorFlow.js, the ml5.js library provides access to machine learning algorithms and models in the browser with a concise, approachable API. Comfortable with concepts like Tensors, Layers, Optimizers and Loss Functions (or willing to get comfortable with them)? TensorFlow.js provides flexible building blocks for neural network programming in JavaScript. -
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Stanza
Stanford NLP Python library for many human languages
... of speech and morphological features, to give a syntactic structure dependency parse, and to recognize named entities. The toolkit is designed to be parallel among more than 70 languages, using the Universal Dependencies formalism. Stanza is built with highly accurate neural network components that also enable efficient training and evaluation with your own annotated data. -
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MIVisionX
Set of comprehensive computer vision & machine intelligence libraries
MIVisionX toolkit is a set of comprehensive computer vision and machine intelligence libraries, utilities, and applications bundled into a single toolkit. AMD MIVisionX delivers highly optimized open-source implementation of the Khronos OpenVX™ and OpenVX™ Extensions along with Convolution Neural Net Model Compiler & Optimizer supporting ONNX, and Khronos NNEF™ exchange formats. The toolkit allows for rapid prototyping and deployment of optimized computer vision and machine learning inference... -
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Qdrant
Vector Database for the next generation of AI applications
Qdrant is a vector similarity engine & vector database. It deploys as an API service providing search for the nearest high-dimensional vectors. With Qdrant, embeddings or neural network encoders can be turned into full-fledged applications for matching, searching, recommending, and much more! Provides the OpenAPI v3 specification to generate a client library in almost any programming language. Alternatively, utilize ready-made client for Python or other programming languages with additional... -
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DeepLabCut
Implementation of DeepLabCut
DeepLabCut™ is an efficient method for 2D and 3D markerless pose estimation based on transfer learning with deep neural networks that achieves excellent results (i.e. you can match human labeling accuracy) with minimal training data (typically 50-200 frames). We demonstrate the versatility of this framework by tracking various body parts in multiple species across a broad collection of behaviors. The package is open source, fast, robust, and can be used to compute 3D pose estimates... -
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Bittensor
Internet-scale Neural Networks
Bittensor is a decentralized machine learning protocol that allows AI models to collaborate, learn, and earn tokens within a global network. It introduces a blockchain-based economy for neural networks, where participants are incentivized to contribute valuable knowledge and compute power. Bittensor combines peer-to-peer learning with on-chain rewards, creating a self-governing, scalable AI system that evolves without centralized control. It is a novel approach to aligning incentives in AI... -
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CoreNet
CoreNet: A library for training deep neural networks
CoreNet is Apple’s internal deep learning framework for distributed neural network training, designed for high scalability, low-latency communication, and strong hardware efficiency. It focuses on enabling large-scale model training across clusters of GPUs and accelerators by optimizing data flow and parallelism strategies. CoreNet provides abstractions for data, tensor, and pipeline parallelism, allowing models to scale without code duplication or heavy manual configuration. Its distributed... -
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MMDeploy
OpenMMLab Model Deployment Framework
MMDeploy is an open-source deep learning model deployment toolset. It is a part of the OpenMMLab project. Models can be exported and run in several backends, and more will be compatible. All kinds of modules in the SDK can be extended, such as Transform for image processing, Net for Neural Network inference, Module for postprocessing and so on. Install and build your target backend. ONNX Runtime is a cross-platform inference and training accelerator compatible with many popular ML/DNN... -
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Basic Pitch
A lightweight audio-to-MIDI converter with pitch bend detection
Basic Pitch is a Python library for Automatic Music Transcription (AMT), using lightweight neural network developed by Spotify's Audio Intelligence Lab. It's small, easy-to-use, pip install-able and npm install-able via its sibling repo. Basic Pitch may be simple, but it's is far from "basic"! basic-pitch is efficient and easy to use, and its multi pitch support, its ability to generalize across instruments, and its note accuracy compete with much larger and more resource-hungry AMT systems... -
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Axon
Nx-powered Neural Networks
... abstractions that enable easy integration while maintaining a level of separation between each component. You should be able to use any of the APIs without dependencies on others. By decoupling the APIs, Axon gives you full control over each aspect of creating and training a neural network. At the lowest-level, Axon consists of a number of modules with functional implementations of common methods in deep learning. -
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Gen.jl
A general-purpose probabilistic programming system
... for writing down generative models, inference models, variational families, and proposal distributions using ordinary code. But it also lets users migrate parts of their model or inference algorithm to specialized modeling languages for which it can generate especially fast code. Users can also hand-code parts of their models that demand better performance. Neural network inference is fast, but can be inaccurate on out-of-distribution data, and requires expensive training. -
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PyG
Graph Neural Network Library for PyTorch
PyG (PyTorch Geometric) is a library built upon PyTorch to easily write and train Graph Neural Networks (GNNs) for a wide range of applications related to structured data. It consists of various methods for deep learning on graphs and other irregular structures, also known as geometric deep learning, from a variety of published papers. In addition, it consists of easy-to-use mini-batch loaders for operating on many small and single giant graphs, multi GPU-support, DataPipe support, distributed... -
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Penzai
A JAX research toolkit to build, edit, & visualize neural networks
... inspection and modification after training. Its modular design includes tools for tree manipulation, named axes, and declarative neural network construction. The library integrates tightly with Treescope, an advanced pretty-printer for visualizing deeply nested JAX pytrees and NDArray structures. Penzai’s penzai.nn module provides a compositional, combinator-based API for building neural networks.
