Showing 497 open source projects for "linux-secure"

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  • 1
    Ray

    Ray

    A unified framework for scalable computing

    Modern workloads like deep learning and hyperparameter tuning are compute-intensive and require distributed or parallel execution. Ray makes it effortless to parallelize single machine code — go from a single CPU to multi-core, multi-GPU or multi-node with minimal code changes. Accelerate your PyTorch and Tensorflow workload with a more resource-efficient and flexible distributed execution framework powered by Ray. Accelerate your hyperparameter search workloads with Ray Tune. Find the best...
    Downloads: 1 This Week
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  • 2
    DeepPavlov

    DeepPavlov

    A library for deep learning end-to-end dialog systems and chatbots

    DeepPavlov makes it easy for beginners and experts to create dialogue systems. The best place to start is with user-friendly tutorials. They provide quick and convenient introduction on how to use DeepPavlov with complete, end-to-end examples. No installation needed. Guides explain the concepts and components of DeepPavlov. Follow step-by-step instructions to install, configure and extend DeepPavlov framework for your use case. DeepPavlov is an open-source framework for chatbots and virtual...
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  • 3
    ChatterBot

    ChatterBot

    Machine learning, conversational dialog engine for creating chat bots

    ChatterBot is a Python library that makes it easy to generate automated responses to a user’s input. ChatterBot uses a selection of machine learning algorithms to produce different types of responses. This makes it easy for developers to create chat bots and automate conversations with users. For more details about the ideas and concepts behind ChatterBot see the process flow diagram. The language independent design of ChatterBot allows it to be trained to speak any language. Additionally,...
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  • 4
    Lightweight' GAN

    Lightweight' GAN

    Implementation of 'lightweight' GAN, proposed in ICLR 2021

    Implementation of 'lightweight' GAN proposed in ICLR 2021, in Pytorch. The main contribution of the paper is a skip-layer excitation in the generator, paired with autoencoding self-supervised learning in the discriminator. Quoting the one-line summary "converge on single gpu with few hours' training, on 1024 resolution sub-hundred images". Augmentation is essential for Lightweight GAN to work effectively in a low data setting. You can test and see how your images will be augmented before...
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  • 5
    Evidently

    Evidently

    Evaluate and monitor ML models from validation to production

    Evidently is an open-source Python library for data scientists and ML engineers. It helps evaluate, test, and monitor ML models from validation to production. It works with tabular, text data and embeddings.
    Downloads: 0 This Week
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  • 6
    Evaluate

    Evaluate

    A library for easily evaluating machine learning models and datasets

    Evaluate is a library that makes evaluating and comparing models and reporting their performance easier and more standardized.
    Downloads: 0 This Week
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  • 7
    SKORCH

    SKORCH

    A scikit-learn compatible neural network library that wraps PyTorch

    A scikit-learn compatible neural network library that wraps PyTorch.
    Downloads: 0 This Week
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  • 8
    Chemprop

    Chemprop

    Message Passing Neural Networks for Molecule Property Prediction

    Chemprop is a repository containing message-passing neural networks for molecular property prediction.
    Downloads: 0 This Week
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  • 9
    Hummingbird

    Hummingbird

    Hummingbird compiles trained ML models into tensor computation

    Hummingbird is a library for compiling trained traditional ML models into tensor computations. Hummingbird allows users to seamlessly leverage neural network frameworks (such as PyTorch) to accelerate traditional ML models. Thanks to Hummingbird, users can benefit from (1) all the current and future optimizations implemented in neural network frameworks; (2) native hardware acceleration; (3) having a unique platform to support both traditional and neural network models; and having all of...
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  • 10
    TorchMetrics AI

    TorchMetrics AI

    Machine learning metrics for distributed, scalable PyTorch application

    TorchMetrics is a collection of 100+ PyTorch metrics implementations and an easy-to-use API to create custom metrics.
    Downloads: 0 This Week
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  • 11
    fklearn

    fklearn

    Functional Machine Learning

    fklearn uses functional programming principles to make it easier to solve real problems with Machine Learning.
    Downloads: 0 This Week
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  • 12
    Mlxtend

    Mlxtend

    A library of extension and helper modules for Python's data analysis

    Mlxtend (machine learning extensions) is a Python library of useful tools for day-to-day data science tasks.
    Downloads: 0 This Week
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  • 13
    imbalanced-learn

    imbalanced-learn

    A Python Package to Tackle the Curse of Imbalanced Datasets in ML

    Imbalanced-learn (imported as imblearn) is an open source, MIT-licensed library relying on scikit-learn (imported as sklearn) and provides tools when dealing with classification with imbalanced classes.
    Downloads: 0 This Week
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  • 14
    Lazy Predict

    Lazy Predict

    Lazy Predict help build a lot of basic models without much code

    Lazy Predict helps build a lot of basic models without much code and helps understand which models work better without any parameter tuning.
    Downloads: 0 This Week
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  • 15
    River ML

    River ML

    Online machine learning in Python

    River is a Python library for online machine learning. It aims to be the most user-friendly library for doing machine learning on streaming data. River is the result of a merger between creme and scikit-multiflow.
    Downloads: 0 This Week
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  • 16
    SageMaker Python SDK

    SageMaker Python SDK

    Training and deploying machine learning models on Amazon SageMaker

    SageMaker Python SDK is an open source library for training and deploying machine learning models on Amazon SageMaker. With the SDK, you can train and deploy models using popular deep learning frameworks Apache MXNet and TensorFlow. You can also train and deploy models with Amazon algorithms, which are scalable implementations of core machine learning algorithms that are optimized for SageMaker and GPU training. If you have your own algorithms built into SageMaker-compatible Docker...
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  • 17
    TensorLy

    TensorLy

    Tensor Learning in Python

    TensorLy is a Python library that aims at making tensor learning simple and accessible. It allows to easily perform tensor decomposition, tensor learning and tensor algebra. Its backend system allows to seamlessly perform computation with NumPy, PyTorch, JAX, TensorFlow, CuPy or Paddle, and run methods at scale on CPU or GPU.
    Downloads: 0 This Week
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  • 18
    pmdarima

    pmdarima

    Statistical library designed to fill the void in Python's time series

    A statistical library designed to fill the void in Python's time series analysis capabilities, including the equivalent of R's auto.arima function.
    Downloads: 0 This Week
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  • 19
    NGBoost

    NGBoost

    Natural Gradient Boosting for Probabilistic Prediction

    ngboost is a Python library that implements Natural Gradient Boosting, as described in "NGBoost: Natural Gradient Boosting for Probabilistic Prediction". It is built on top of Scikit-Learn and is designed to be scalable and modular with respect to the choice of proper scoring rule, distribution, and base learner. A didactic introduction to the methodology underlying NGBoost is available in this slide deck.
    Downloads: 0 This Week
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  • 20
    GPflow

    GPflow

    Gaussian processes in TensorFlow

    GPflow is a package for building Gaussian process models in Python. It implements modern Gaussian process inference for composable kernels and likelihoods. GPflow builds on TensorFlow 2.4+ and TensorFlow Probability for running computations, which allows fast execution on GPUs.
    Downloads: 0 This Week
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  • 21
    Scanpy

    Scanpy

    Single-cell analysis in Python

    Scanpy is a scalable toolkit for analyzing single-cell gene expression data built jointly with anndata. It includes preprocessing, visualization, clustering, trajectory inference and differential expression testing. The Python-based implementation efficiently deals with datasets of more than one million cells.
    Downloads: 0 This Week
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  • 22
    Alibi Detect

    Alibi Detect

    Algorithms for outlier, adversarial and drift detection

    Alibi Detect is an open source Python library focused on outlier, adversarial and drift detection. The package aims to cover both online and offline detectors for tabular data, text, images and time series. Both TensorFlow and PyTorch backends are supported for drift detection.
    Downloads: 0 This Week
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  • 23
    Awesome Fraud Detection Research Papers

    Awesome Fraud Detection Research Papers

    A curated list of data mining papers about fraud detection

    A curated list of data mining papers about fraud detection from several conferences.
    Downloads: 0 This Week
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  • 24
    openTSNE

    openTSNE

    Extensible, parallel implementations of t-SNE

    openTSNE is a modular Python implementation of t-Distributed Stochasitc Neighbor Embedding (t-SNE) [1], a popular dimensionality-reduction algorithm for visualizing high-dimensional data sets. openTSNE incorporates the latest improvements to the t-SNE algorithm, including the ability to add new data points to existing embeddings [2], massive speed improvements [3] [4] [5], enabling t-SNE to scale to millions of data points, and various tricks to improve the global alignment of the resulting...
    Downloads: 0 This Week
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  • 25
    Advanced Solutions Lab

    Advanced Solutions Lab

    This repos contains notebooks for the Advanced Solutions Lab

    This repository contains Jupyter notebooks meant to be run on Vertex AI. This is maintained by Google Cloud’s Advanced Solutions Lab (ASL) team. Vertex AI is the next-generation AI Platform on the Google Cloud Platform. The material covered in this repo will take a software engineer with no exposure to machine learning to an advanced level.
    Downloads: 0 This Week
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