Search Results for "statistical learning"
Sort By:
Showing 8 open source projects for "statistical learning"
View related business solutions-
Paessler: Easy to Use With Enterprise Power. Free TrialYou shouldn't have to choose between a monitoring tool that's easy to use and one that's powerful enough for a complex environment. PRTG's low-code interface lets any admin build dashboards, set alerts and monitor devices without scripting, while custom sensors and full API access are there when your team needs deeper control. One platform, no compromise. Download a free 30-day trial now.
-
Build Agents and Models on One PlatformGemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
-
1
stdlib
Standard library for JavaScript and Node.js
A standard library for javascript and node.js. High performance, rigorous, and robust mathematical and statistical functions. Build advanced statistical models and machine learning libraries. Plotting and graphics functionality for data visualization and exploratory data analysis. Analyze and understand your data. Comprehensively tested utilities for application and library development. Functions to assert, group, filter, map, pluck, and transform your data both in browsers and on the server. ... -
2
spaCy
Industrial-strength Natural Language Processing (NLP)
spaCy is a library built on the very latest research for advanced Natural Language Processing (NLP) in Python and Cython. Since its inception it was designed to be used for real world applications-- for building real products and gathering real insights. It comes with pretrained statistical models and word vectors, convolutional neural network models, easy deep learning integration and so much more. spaCy is the fastest syntactic parser in the world according to independent benchmarks, with an accuracy within 1% of the best available. It's blazing fast, easy to install and comes with a simple and productive API. -
3
Introduction-NLP
Detailed notes on HanLP's new book, "Introduction to Natural Language"
...It explains NLP concepts in accessible language while pairing theory with practical implementation notes. The material begins with basic concepts and Chinese word segmentation before progressing into statistical sequence models. Later chapters cover part-of-speech tagging, named entity recognition, information extraction, text clustering, text classification, dependency parsing, and deep learning. Algorithms discussed include hidden Markov models, perceptrons, conditional random fields, Naive Bayes, and support vector machines. Many chapters link to executable code examples and datasets for hands-on study. ... -
4
Darts
A python library for easy manipulation and forecasting of time series
darts is a Python library for easy manipulation and forecasting of time series. It contains a variety of models, from classics such as ARIMA to deep neural networks. The models can all be used in the same way, using fit() and predict() functions, similar to scikit-learn. The library also makes it easy to backtest models, combine the predictions of several models, and take external data into account. Darts supports both univariate and multivariate time series and models. The ML-based models... -
Train ML Models With SQL You Already KnowBuild and deploy ML models using familiar SQL. Automate data prep with built-in Gemini. Query 1 TB and store 10 GB free monthly.
-
5
LakshmiDROP
Fixed Income Analytics, Portfolio Construction, Asset Backed Cost
DROP implements the model libraries and provides systems for fixed income valuation and adjustments, asset allocation and transaction cost analytics, and supporting libraries in numerical optimization and statistical learning. DROP is composed of four main libraries: [Asset Allocation] (https://lakshmidrip.github.io/DROP/AssetAllocationModule.html) [Fixed Income Analytics] (https://lakshmidrip.github.io/DROP/FixedIncomeModule.html) [Numerical Optimization] (https://lakshmidrip.github.io/DROP/NumericalOptimizerModule.html) [Statistical Learning] (https://lakshmidrip.github.io/DROP/StatisticalLearningModule.html) -
6
ARC-AGI
The Abstraction and Reasoning Corpus
...The dataset is structured as grid-based puzzles, where each task requires understanding transformations such as symmetry, counting, or spatial manipulation. Unlike traditional machine learning benchmarks, ARC emphasizes generalization and reasoning over statistical pattern recognition, making it particularly challenging for current AI systems. The repository also includes a browser-based interface that allows humans to attempt solving the tasks manually, providing a baseline for comparison. -
7
libpostal
A C library for parsing/normalizing street addresses around the world
A C library for parsing/normalizing street addresses around the world. Powered by statistical NLP and open geo data. libpostal is a C library for parsing/normalizing street addresses around the world using statistical NLP and open data. The goal of this project is to understand location-based strings in every language, everywhere. Addresses and the locations they represent are essential for any application dealing with maps (place search, transportation, on-demand/delivery services,... -
8
Accord.NET Framework
Scientific computing, machine learning and computer vision for .NET
The Accord.NET Framework provides machine learning, mathematics, statistics, computer vision, computer audition, and several scientific computing related methods and techniques to .NET. The project is compatible with the .NET Framework. NET Standard, .NET Core, and Mono.
- Previous
- You're on page 1
- Next
