Search Results for "support vector machine" - Page 8

Showing 773 open source projects for "support vector machine"

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  • 1
    Llama Recipes

    Llama Recipes

    Scripts for fine-tuning Meta Llama3 with composable FSDP & PEFT method

    The 'llama-recipes' repository is a companion to the Meta Llama models. We support the latest version, Llama 3.1, in this repository. The goal is to provide a scalable library for fine-tuning Meta Llama models, along with some example scripts and notebooks to quickly get started with using the models in a variety of use-cases, including fine-tuning for domain adaptation and building LLM-based applications with Llama and other tools in the LLM ecosystem. The examples here showcase how to run...
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  • 2
    go-i18n

    go-i18n

    Translate your Go program into multiple languages

    go-i18n is a Go package and a command that helps you translate Go programs into multiple languages. Supports pluralized strings for all 200+ languages in the Unicode Common Locale Data Repository (CLDR). Code and tests are automatically generated from CLDR data. Supports strings with named variables using text/template syntax. Supports message files of any format (e.g. JSON, TOML, YAML). Use goi18n extract to extract all i18n.Message struct literals in Go source files to a message file for...
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  • 3
    Dragonboat

    Dragonboat

    A feature complete and high performance multi-group Raft library in Go

    .... Dragonboat handles all technical difficulties associated with Raft to allow users to just focus on their application domains. It is also very easy to use, our step-by-step examples can help new users to master it in half an hour. Easy to use pure-Go APIs for building Raft based applications. Feature complete and scalable multi-group Raft implementation. Disk based and memory based state machine support. Fully pipelined and TLS mutual authentication support.
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  • 4
    Carmine

    Carmine

    Redis client and message queue for Clojure

    ... and encryption support (v2+). Includes Tundra, an API for replicating data to an additional datastore (v2+, Redis 2.6+). You'll usually want to define a single connection pool, and one connection spec for each of your Redis servers. Note that executing multiple Redis commands in a single wcar request uses efficient Redis pipelining under the hood, and returns a pipeline reply (vector) for easy destructuring, etc.
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  • 5
    supercluster

    supercluster

    A very fast geospatial point clustering library for browsers and Node

    A very fast JavaScript library for geospatial point clustering for browsers and Node. supercluster supports property aggregation with the following two options. Map, a function that returns cluster properties corresponding to a single point. Reduce, a reduce function that merges properties of two clusters into one.
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  • 6
    Superduper

    Superduper

    Superduper: Integrate AI models and machine learning workflows

    Superduper is a Python-based framework for building end-2-end AI-data workflows and applications on your own data, integrating with major databases. It supports the latest technologies and techniques, including LLMs, vector-search, RAG, and multimodality as well as classical AI and ML paradigms. Developers may leverage Superduper by building compositional and declarative objects that out-source the details of deployment, orchestration versioning, and more to the Superduper engine. This allows...
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  • 7
    Quickwit

    Quickwit

    Sub-second search & analytics engine on cloud storage

    Sub-second search & analytics engine on cloud storage. Quickwit is the fastest search engine on cloud storage. Quickwit has an Elasticsearch-compatible Ingest-API to make it easier to migrate your log shippers (Vector, Fluent Bit, Syslog, ...) to Quickwit. However, we only support ES aggregation DSL, the query DSL support is planned for Q2 2023. The core difference and advantage of Quickwit are its architecture built from the ground to search on cloud storage. We optimized IO paths, revamped...
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  • 8
    spaGO

    spaGO

    Self-contained Machine Learning and Natural Language Processing lib

    A Machine Learning library written in pure Go designed to support relevant neural architectures in Natural Language Processing. Spago is self-contained, in that it uses its own lightweight computational graph both for training and inference, easy to understand from start to finish. The core module of Spago relies only on testify for unit testing. In other words, it has "zero dependencies", and we are committed to keeping it that way as much as possible. Spago uses a multi-module workspace...
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  • 9
    PennyLane

    PennyLane

    A cross-platform Python library for differentiable programming

    A cross-platform Python library for differentiable programming of quantum computers. Train a quantum computer the same way as a neural network. Built-in automatic differentiation of quantum circuits, using the near-term quantum devices directly. You can combine multiple quantum devices with classical processing arbitrarily! Support for hybrid quantum and classical models, and compatible with existing machine learning libraries. Quantum circuits can be set up to interface with either NumPy...
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  • 10
    SHAP

    SHAP

    A game theoretic approach to explain the output of ml models

    SHAP (SHapley Additive exPlanations) is a game theoretic approach to explain the output of any machine learning model. It connects optimal credit allocation with local explanations using the classic Shapley values from game theory and their related extensions. While SHAP can explain the output of any machine learning model, we have developed a high-speed exact algorithm for tree ensemble methods. Fast C++ implementations are supported for XGBoost, LightGBM, CatBoost, scikit-learn and pyspark...
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  • 11
    Transformer Engine

    Transformer Engine

    A library for accelerating Transformer models on NVIDIA GPUs

    ... that can be integrated with other deep-learning libraries to enable FP8 support for Transformers. As the number of parameters in Transformer models continues to grow, training and inference for architectures such as BERT, GPT, and T5 become very memory and compute-intensive. Most deep learning frameworks train with FP32 by default. This is not essential, however, to achieve full accuracy for many deep learning models.
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  • 12
    Flux.jl

    Flux.jl

    Relax! Flux is the ML library that doesn't make you tensor

    Flux is an elegant approach to machine learning. It's a 100% pure Julia stack and provides lightweight abstractions on top of Julia's native GPU and AD support. Flux makes the easy things easy while remaining fully hackable. Flux provides a single, intuitive way to define models, just like mathematical notation. Julia transparently compiles your code, optimizing and fusing kernels for the GPU, for the best performance. Existing Julia libraries are differentiable and can be incorporated directly...
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  • 13
    chezmoi

    chezmoi

    Manage your dotfiles across multiple diverse machines, securely

    Manage your dotfiles across multiple diverse machines, securely. chezmoi helps you manage your personal configuration files (dotfiles, like ~/.gitconfig) across multiple machines. chezmoi provides many features beyond symlinking or using a bare git repo including templates (to handle small differences between machines), password manager support (to store your secrets securely), importing files from archives (great for shell and editor plugins), full file encryption (using gpg or age...
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  • 14
    Elyra

    Elyra

    Elyra extends JupyterLab with an AI centric approach

    Elyra is a set of AI-centric extensions to JupyterLab Notebooks. The Elyra Getting Started Guide includes more details on these features. A version-specific summary of new features is located on the releases page.
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  • 15
    The HaskellR project

    The HaskellR project

    The full power of R in Haskell

    The HaskellR project provides an environment for efficiently processing data using Haskell or R code, interchangeably. HaskellR allows Haskell functions to seamlessly call R functions and vice versa. It provides the Haskell programmer with the full breadth of existing R libraries and extensions for numerical computation, statistical analysis and machine learning. Optionally, pass in the --nix flag to all commands if you have the Nix package manager installed. Nix can populate a local build...
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  • 16
    pycm

    pycm

    Multi-class confusion matrix library in Python

    PyCM is a multi-class confusion matrix library written in Python that supports both input data vectors and direct matrix, and a proper tool for post-classification model evaluation that supports most classes and overall statistics parameters. PyCM is the swiss-army knife of confusion matrices, targeted mainly at data scientists that need a broad array of metrics for predictive models and an accurate evaluation of large variety of classifiers.
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  • 17
    PyMC3

    PyMC3

    Probabilistic programming in Python

    ... in a model is not a scalar value or a fixed-length vector, but a function. A Gaussian process (GP) can be used as a prior probability distribution whose support is over the space of continuous functions. PyMC3 provides rich support for defining and using GPs. Variational inference saves computational cost by turning a problem of integration into one of optimization. PyMC3's variational API supports a number of cutting edge algorithms, as well as minibatch for scaling to large datasets.
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  • 18
    Qiling

    Qiling

    Qiling Advanced Binary Emulation Framework

    Cross-platform and multi-arch ultra lightweight emulator. Supported OS: Linux, MacOS, Windows, FreeBSD, DOS and UEFI. Support Arch: x86(16/32/64), ARM(64) MIPS, EVM and WASM. It also support Linux Kernel Module(.ko) , Windows Driver(.sys) and MacOS Kernel(.kext) via Demigod. Binary instrumentation and API are Qiling Framework's main focus and priority. It is designed for reverse engineers - thus there is no need to rebuild another sand boxing tool. Using Qiling Framework saves you time. The API...
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  • 19
    PyTorch Implementation of SDE Solvers

    PyTorch Implementation of SDE Solvers

    Differentiable SDE solvers with GPU support and efficient sensitivity

    This library provides stochastic differential equation (SDE) solvers with GPU support and efficient backpropagation. examples/demo.ipynb gives a short guide on how to solve SDEs, including subtle points such as fixing the randomness in the solver and the choice of noise types. examples/latent_sde.py learns a latent stochastic differential equation, as in Section 5 of [1]. The example fits an SDE to data, whilst regularizing it to be like an Ornstein-Uhlenbeck prior process. The model can...
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  • 20
    TensorRT Backend For ONNX

    TensorRT Backend For ONNX

    ONNX-TensorRT: TensorRT backend for ONNX

    Parses ONNX models for execution with TensorRT. Development on the main branch is for the latest version of TensorRT 8.4.1.5 with full dimensions and dynamic shape support. For previous versions of TensorRT, refer to their respective branches. Building INetwork objects in full dimensions mode with dynamic shape support requires calling the C++ and Python API. Current supported ONNX operators are found in the operator support matrix. For building within docker, we recommend using and setting up...
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  • 21
    MMAction2

    MMAction2

    OpenMMLab's Next Generation Video Understanding Toolbox and Benchmark

    OpenMMLab's next generation video understanding toolbox and benchmark. MMAction2 is an open-source toolbox for video understanding based on PyTorch. It is a part of the OpenMMLab project. Modular design: We decompose a video understanding framework into different components. One can easily construct a customized video understanding framework by combining different modules. Support four major video understanding tasks: MMAction2 implements various algorithms for multiple video understanding...
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  • 22
    ko

    ko

    Build and deploy Go applications on Kubernetes

    ko is a simple, fast container image builder for Go applications. It's ideal for use cases where your image contains a single Go application without any/many dependencies on the OS base image (e.g., no cgo, no OS package dependencies). ko builds images by effectively executing go build on your local machine, and as such doesn't require docker to be installed. This can make it a good fit for lightweight CI/CD use cases. ko also includes support for simple YAML templating which makes...
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  • 23
    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.
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  • 24
    GraphNeuralNetworks.jl

    GraphNeuralNetworks.jl

    Graph Neural Networks in Julia

    GraphNeuralNetworks.jl is a graph neural network library written in Julia and based on the deep learning framework Flux.jl.
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  • 25
    Spark NLP

    Spark NLP

    State of the Art Natural Language Processing

    Experience the power of large language models like never before, unleashing the full potential of Natural Language Processing (NLP) with Spark NLP, the open source library that delivers scalable LLMs. The full code base is open under the Apache 2.0 license, including pre-trained models and pipelines. The only NLP library built natively on Apache Spark. The most widely used NLP library in the enterprise. Spark ML provides a set of machine learning applications that can be built using two main...
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