Showing 74 open source projects for "python neural"

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  • 1
    Synapse Machine Learning

    Synapse Machine Learning

    Simple and distributed Machine Learning

    SynapseML (previously MMLSpark) is an open source library to simplify the creation of scalable machine learning pipelines. SynapseML builds on Apache Spark and SparkML to enable new kinds of machine learning, analytics, and model deployment workflows. SynapseML adds many deep learning and data science tools to the Spark ecosystem, including seamless integration of Spark Machine Learning pipelines with the Open Neural Network Exchange (ONNX), LightGBM, The Cognitive Services, Vowpal Wabbit...
    Downloads: 2 This Week
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  • 2
    Albumentations

    Albumentations

    Fast image augmentation library and an easy-to-use wrapper

    Albumentations is a computer vision tool that boosts the performance of deep convolutional neural networks. Albumentations is a Python library for fast and flexible image augmentations. Albumentations efficiently implements a rich variety of image transform operations that are optimized for performance, and does so while providing a concise, yet powerful image augmentation interface for different computer vision tasks, including object classification, segmentation, and detection. Albumentations...
    Downloads: 3 This Week
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  • 3
    hloc

    hloc

    Visual localization made easy with hloc

    This is hloc, a modular toolbox for state-of-the-art 6-DoF visual localization. It implements Hierarchical Localization, leveraging image retrieval and feature matching, and is fast, accurate, and scalable. This codebase won the indoor/outdoor localization challenges at CVPR 2020 and ECCV 2020, in combination with SuperGlue, our graph neural network for feature matching. We provide step-by-step guides to localize with Aachen, InLoc, and to generate reference poses for your own data using SfM...
    Downloads: 5 This Week
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  • 4
    NNCF

    NNCF

    Neural Network Compression Framework for enhanced OpenVINO

    NNCF (Neural Network Compression Framework) is an optimization toolkit for deep learning models, designed to apply quantization, pruning, and other techniques to improve inference efficiency.
    Downloads: 1 This Week
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  • 5
    Haiku

    Haiku

    JAX-based neural network library

    Haiku is a library built on top of JAX designed to provide simple, composable abstractions for machine learning research. Haiku is a simple neural network library for JAX that enables users to use familiar object-oriented programming models while allowing full access to JAX’s pure function transformations. Haiku is designed to make the common things we do such as managing model parameters and other model state simpler and similar in spirit to the Sonnet library that has been widely used across...
    Downloads: 0 This Week
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  • 6
    Jina

    Jina

    Build cross-modal and multimodal applications on the cloud

    Jina is a framework that empowers anyone to build cross-modal and multi-modal applications on the cloud. It uplifts a PoC into a production-ready service. Jina handles the infrastructure complexity, making advanced solution engineering and cloud-native technologies accessible to every developer. Build applications that deliver fresh insights from multiple data types such as text, image, audio, video, 3D mesh, PDF with Jina AI’s DocArray. Polyglot gateway that supports gRPC, Websockets, HTTP,...
    Downloads: 1 This Week
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  • 7
    PyTorch Geometric

    PyTorch Geometric

    Geometric deep learning extension library for PyTorch

    ... of functionality of PyTorch Geometric to other packages, which needs to be additionally installed. These packages come with their own CPU and GPU kernel implementations based on C++/CUDA extensions. We do not recommend installation as root user on your system python. Please setup an Anaconda/Miniconda environment or create a Docker image. We provide pip wheels for all major OS/PyTorch/CUDA combinations.
    Downloads: 1 This Week
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  • 8
    tinygrad

    tinygrad

    Deep learning framework

    This may not be the best deep learning framework, but it is a deep learning framework. Due to its extreme simplicity, it aims to be the easiest framework to add new accelerators to, with support for both inference and training. If XLA is CISC, tinygrad is RISC.
    Downloads: 0 This Week
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  • 9
    Opacus

    Opacus

    Training PyTorch models with differential privacy

    Opacus is a library that enables training PyTorch models with differential privacy. It supports training with minimal code changes required on the client, has little impact on training performance, and allows the client to online track the privacy budget expended at any given moment. Vectorized per-sample gradient computation that is 10x faster than micro batching. Supports most types of PyTorch models and can be used with minimal modification to the original neural network. Open source...
    Downloads: 0 This Week
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  • 10
    towhee

    towhee

    Framework that is dedicated to making neural data processing

    Towhee is an open-source machine-learning pipeline that helps you encode your unstructured data into embeddings. You can use our Python API to build a prototype of your pipeline and use Towhee to automatically optimize it for production-ready environments. From images to text to 3D molecular structures, Towhee supports data transformation for nearly 20 different unstructured data modalities. We provide end-to-end pipeline optimizations, covering everything from data decoding/encoding, to model...
    Downloads: 0 This Week
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  • 11
    tslab

    tslab

    Interactive JavaScript and TypeScript programming with Jupyter

    tslab is an interactive programming environment and REPL with Jupyter for JavaScript and TypeScript users. You can write and execute JavaScript and TypeScript interactively on browsers and save results as Jupyter notebooks.
    Downloads: 0 This Week
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  • 12
    Alpa

    Alpa

    Training and serving large-scale neural networks

    Alpa is a system for training and serving large-scale neural networks. Scaling neural networks to hundreds of billions of parameters has enabled dramatic breakthroughs such as GPT-3, but training and serving these large-scale neural networks require complicated distributed system techniques. Alpa aims to automate large-scale distributed training and serving with just a few lines of code.
    Downloads: 10 This Week
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  • 13
    DIG

    DIG

    A library for graph deep learning research

    The key difference with current graph deep learning libraries, such as PyTorch Geometric (PyG) and Deep Graph Library (DGL), is that, while PyG and DGL support basic graph deep learning operations, DIG provides a unified testbed for higher level, research-oriented graph deep learning tasks, such as graph generation, self-supervised learning, explainability, 3D graphs, and graph out-of-distribution. If you are working or plan to work on research in graph deep learning, DIG enables you to...
    Downloads: 2 This Week
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  • 14
    AlphaZero.jl

    AlphaZero.jl

    A generic, simple and fast implementation of Deepmind's AlphaZero

    ...++) and optimized for highly distributed computing environments. This makes them hardly accessible for students, researchers and hackers. Many simple Python implementations can be found on Github, but none of them is able to beat a reasonable baseline on games such as Othello or Connect Four. As an illustration, the benchmark in the README of the most popular of them only features a random baseline, along with a greedy baseline that does not appear to be significantly stronger.
    Downloads: 47 This Week
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  • 15
    DeepCTR

    DeepCTR

    Package of deep-learning based CTR models

    DeepCTR is a Easy-to-use,Modular and Extendible package of deep-learning based CTR models along with lots of core components layers which can be used to easily build custom models. You can use any complex model with model.fit(), and model.predict(). Provide tf.keras.Model like interface for quick experiment. Provide tensorflow estimator interface for large scale data and distributed training. It is compatible with both tf 1.x and tf 2.x. With the great success of deep learning,DNN-based...
    Downloads: 2 This Week
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  • 16
    MLPACK is a C++ machine learning library with emphasis on scalability, speed, and ease-of-use. Its aim is to make machine learning possible for novice users by means of a simple, consistent API, while simultaneously exploiting C++ language features to provide maximum performance and flexibility for expert users. * More info + downloads: https://mlpack.org * Git repo: https://github.com/mlpack/mlpack
    Downloads: 0 This Week
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  • 17
    DeepCTR-Torch

    DeepCTR-Torch

    Easy-to-use,Modular and Extendible package of deep-learning models

    ... features and some dense numerical features. Low-order Extractor learns feature interaction through product between vectors. Factorization-Machine and it’s variants are widely used to learn the low-order feature interaction. High-order Extractor learns feature combination through complex neural network functions like MLP, Cross Net, etc.
    Downloads: 2 This Week
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  • 18

    Proteus Model Builder

    GUI for training of neural network models for GuitarML Proteus

    GUI for easier installation and training of neural network models for guitar amplifiers and pedals, based on the GuitarML Proteus models. These are usable for Proteus, Chowdhury-DSP BYOD and even NeuralPi, on all platforms incl. Linux and RaspberryPi. What is this? GuitarML's work on Proteus, NeuralPi and Proteusboard (hardware) is amazing. https://github.com/GuitarML Yet, it is not easy to wrap your head around if you are not familiar with programming, AI, machine learning, neuronal...
    Downloads: 8 This Week
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  • 19
    Fairseq

    Fairseq

    Facebook AI Research Sequence-to-Sequence Toolkit written in Python

    ... in the new FullyShardedDataParallel (FSDP) wrapper provided by fairscale. Fairseq can be extended through user-supplied plug-ins. Models define the neural network architecture and encapsulate all of the learnable parameters. Criterions compute the loss function given the model outputs and targets. Tasks store dictionaries and provide helpers for loading/iterating over Datasets, initializing the Model/Criterion and calculating the loss.
    Downloads: 1 This Week
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  • 20
    Deep Daze

    Deep Daze

    Simple command line tool for text to image generation

    Simple command-line tool for text to image generation using OpenAI's CLIP and Siren (Implicit neural representation network). In true deep learning fashion, more layers will yield better results. Default is at 16, but can be increased to 32 depending on your resources. Technique first devised and shared by Mario Klingemann, it allows you to prime the generator network with a starting image, before being steered towards the text. Simply specify the path to the image you wish to use...
    Downloads: 6 This Week
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  • 21
    Arraymancer

    Arraymancer

    A fast, ergonomic and portable tensor library in Nim

    Arraymancer is a tensor and deep learning library for the Nim programming language, designed for high-performance numerical computations and machine learning applications.
    Downloads: 3 This Week
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  • 22
    Minkowski Engine

    Minkowski Engine

    Auto-diff neural network library for high-dimensional sparse tensors

    The Minkowski Engine is an auto-differentiation library for sparse tensors. It supports all standard neural network layers such as convolution, pooling, unspooling, and broadcasting operations for sparse tensors. The Minkowski Engine supports various functions that can be built on a sparse tensor. We list a few popular network architectures and applications here. To run the examples, please install the package and run the command in the package root directory. Compressing a neural network...
    Downloads: 0 This Week
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  • 23
    PyTorch SimCLR

    PyTorch SimCLR

    PyTorch implementation of SimCLR: A Simple Framework

    For quite some time now, we know about the benefits of transfer learning in Computer Vision (CV) applications. Nowadays, pre-trained Deep Convolution Neural Networks (DCNNs) are the first go-to pre-solutions to learn a new task. These large models are trained on huge supervised corpora, like the ImageNet. And most important, their features are known to adapt well to new problems. This is particularly interesting when annotated training data is scarce. In situations like this, we take the models...
    Downloads: 0 This Week
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  • 24
    TFLearn

    TFLearn

    Deep learning library featuring a higher-level API for TensorFlow

    TFlearn is a modular and transparent deep learning library built on top of Tensorflow. It was designed to provide a higher-level API to TensorFlow in order to facilitate and speed up experimentations while remaining fully transparent and compatible with it. Easy-to-use and understand high-level API for implementing deep neural networks, with tutorials and examples. Fast prototyping through highly modular built-in neural network layers, regularizers, optimizers, and metrics. Full transparency...
    Downloads: 2 This Week
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  • 25
    NLP Architect

    NLP Architect

    A model library for exploring state-of-the-art deep learning

    NLP Architect is an open-source Python library for exploring state-of-the-art deep learning topologies and techniques for optimizing Natural Language Processing and Natural Language Understanding neural networks. The library includes our past and ongoing NLP research and development efforts as part of Intel AI Lab. NLP Architect is designed to be flexible for adding new models, neural network components, data handling methods, and for easy training and running models. NLP Architect is a model...
    Downloads: 0 This Week
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