Search Results for "machine" - Page 95
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Showing 4715 open source projects for "machine"
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MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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TensorFlow Docs
TensorFlow latest official documentation Chinese version
TensorFlow Docs repository maintained by the Xitu translation community provides a Chinese version of the official TensorFlow documentation. Its goal is to make the extensive TensorFlow ecosystem more accessible to developers and researchers who prefer to learn in Chinese. The repository contains translated guides, API explanations, tutorials, and conceptual documentation that mirror the structure of the original TensorFlow documentation site. Contributors from technology companies,... -
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YouTube Video Downloader
Allows you to download youtube videos into a video/audio format.
YouTube Video Downloader By Chase, This is a tool developed in python, by web scraping I can get the videos from YouTube and download it on my machine in a video/audio format, easy-to-use GUI for your needs, dark theme. -
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easy12306
Automatic recognition of 12306 verification code
Automatic recognition of 12306 verification code using machine learning algorithm. Identify never-before-seen pictures. -
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spark-ml-source-analysis
Spark ml algorithm principle analysis and specific source code
spark-ml-source-analysis is a technical repository that analyzes the internal implementation of machine learning algorithms within Apache Spark’s MLlib library. The project aims to help developers and data scientists understand how distributed machine learning algorithms are implemented and optimized inside the Spark ecosystem. Instead of providing a runnable software system, the repository focuses on explaining algorithm principles and examining the underlying source code used in Spark’s machine learning package. ... -
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MUSE
A library for Multilingual Unsupervised or Supervised word Embeddings
...The training and evaluation pipeline is lightweight and fast, so experimenting with different languages or initialization strategies is easy. Beyond dictionary induction, the learned embeddings are often used as building blocks for downstream tasks like classification, retrieval, or machine translation. -
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TenorSpace.js
Neural network 3D visualization framework
TensorSpace is a neural network 3D visualization framework built using TensorFlow.js, Three.js and Tween.js. TensorSpace provides Keras-like APIs to build deep learning layers, load pre-trained models, and generate a 3D visualization in the browser. From TensorSpace, it is intuitive to learn what the model structure is, how the model is trained and how the model predicts the results based on the intermediate information. After preprocessing the model, TensorSpace supports the visualization... -
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Papers with Code
List of different papers for coding
pwc is an open-source repository that compiles machine learning and artificial intelligence research papers together with their corresponding implementation code. The project functions as a curated dataset linking academic publications with practical software implementations, allowing researchers and engineers to quickly locate code that reproduces published results. The repository organizes information such as paper titles, conferences, and links to code implementations so that users can explore recent developments in machine learning more efficiently. ... -
PRTG Catches Network Issues Before They Cause DowntimeReactive troubleshooting usually means hearing about a problem from frustrated users, not your monitoring tool. PRTG sets threshold-based alerts across devices, servers and applications, notifying your team by email, SMS or push the moment a metric crosses a set limit. That means catching a failing disk or overloaded server before it becomes an outage and getting time back from firefighting. Start a free trial and set your first alerts today.
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NeuralCoref
Fast Coreference Resolution in spaCy with Neural Networks
NeuralCoref is a pipeline extension for spaCy 2.1+ which annotates and resolves coreference clusters using a neural network. NeuralCoref is production-ready, integrated in spaCy's NLP pipeline and extensible to new training datasets. For a brief introduction to coreference resolution and NeuralCoref, please refer to our blog post. NeuralCoref is written in Python/Cython and comes with a pre-trained statistical model for English only. NeuralCoref is accompanied by a visualization client... -
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automl-gs
Provide an input CSV and a target field to predict, generate a model
Give an input CSV file and a target field you want to predict to automl-gs, and get a trained high-performing machine learning or deep learning model plus native Python code pipelines allowing you to integrate that model into any prediction workflow. No black box: you can see exactly how the data is processed, and how the model is constructed, and you can make tweaks as necessary. automl-gs is an AutoML tool which, unlike Microsoft's NNI, Uber's Ludwig, and TPOT, offers a zero code/model definition interface to getting an optimized model and data transformation pipeline in multiple popular ML/DL frameworks, with minimal Python dependencies (pandas + scikit-learn + your framework of choice). automl-gs is designed for citizen data scientists and engineers without a deep statistical background under the philosophy that you don't need to know any modern data preprocessing and machine learning engineering techniques to create a powerful prediction workflow. -
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Fuzzy Ecospace Modelling
FEM allows users to create fuzzy functional groups for use in ecology.
Fuzzy Ecospace Modelling (FEM) is an R-based program for quantifying and comparing functional disparity, using a fuzzy set theory-based machine learning approach. FEM clusters n-dimensional matrices of functional traits (ecospace matrices – here called the Training Matrix) into functional groups and converts them into fuzzy functional groups using fuzzy discriminant analysis (Lin and Chen 2004 – see main text for more information). Following this, FEM classifies the functional entities from a second matrix (the Test Matrix) into the groups made using the Training Matrix, generating fuzzy membership values for each unit in the Test Matrix. ... -
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TensorFlow.jl
A Julia wrapper for TensorFlow
A wrapper around TensorFlow, a popular open-source machine learning framework from Google. -
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TradingView Chart Data Extractor
Extract price and indicator data from TradingView charts
...Too many indicators or too low a time resolution will increase the data points and potentially overload the free server. Avoid this by hosting/running the script on your local machine or scraping multiple times with fewer indicators and manually combining the CSV afterward. Simply append the URL of a chart/idea published on TradingView to the link below. This is not the URL of a security's chart, but the URL for a user-published chart. -
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Objective Function Analysis
An alternative to neural nets for machine learning.
Objective Function Analysis models knowledge as a multi-dimensional probability density function (MD-PDF) of the perceptions and responses (which are themselves perceptions) of an entity and an objective function (OF). The learning algorithm is the action of choosing a response, given the perceptions, which maximizes the objective function. The MD-PDF is initially seeded by a uniform random number generator. The response is used to evaluate the OF and the OF is either reinforced or... -
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GUN
Cybersecurity protocol for syncing decentralized graph data
...GUN is fully decentralized (peer-to-peer or multi-master), meaning that changes are not controlled by a centralized server. A server can be just another peer in the network, one that may have more reliable resources than a browser. You save data on one machine, and it will sync it to other peers without needing a complex consensus protocol. It just works. GUN can be used in both browsers and servers. We have made it easy to install in many different environments. -
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DS-Take-Home
Solution to the book A Collection of Data Science Take-Home Challenge
...The project is designed as a learning resource where aspiring data scientists can study how typical industry-style take-home assignments are solved using data analysis and machine learning techniques. Each challenge is implemented in a separate Jupyter notebook that walks through the process of analyzing datasets, performing exploratory data analysis, building predictive models, and interpreting results. The problems cover a broad set of applied data science topics including conversion rate analysis, fraud detection, employee retention modeling, marketing campaign evaluation, and recommendation-style problems. -
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Rasa Core
Rasa Core is now part of the Rasa repo
Rasa is an open source machine learning framework to automate text and voice-based conversations. With Rasa, you can build contextual assistants. Rasa helps you build contextual assistants capable of having layered conversations with lots of back-and-forth. In order for a human to have a meaningful exchange with a contextual assistant, the assistant needs to be able to use context to build on things that were previously discussed – Rasa enables you to build assistants that can do this in a scalable way. -
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Der kleine Halbling
Ein Remake des Grafik-Adventures für den VC-20 aus dem Jahre 1985
Du bist der kleine Halbling. Du musst ein sagenhaftes Land bereisen, Rätsel lösen, Gefahren bestehen und schließlich dem Drachen gegenübertreten. -
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NeuroNER
Named-entity recognition using neural networks
...Identified entities can be used in various downstream applications such as patient note de-identification and information extraction systems. They can also be used as features for machine learning systems for other natural language processing tasks. Leverages the state-of-the-art prediction capabilities of neural networks (a.k.a. "deep learning") Is cross-platform, open source, freely available, and straightforward to use. Enables the users to create or modify annotations for a new or existing corpus. Train the neural network that performs the NER. ... -
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CodelPlant
Codelplant stat. multi-regression M-learning for leaves segmentation
CodelPlant is based on the creation of a statistical multi-regression decision tree from data obtained by pixel RGB-HSB machine learning analysis. The model is training with human observation, so the human factor is being entered the process. -
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SSD
A PyTorch Implementation of Single Shot MultiBox Detector
SSD is a PyTorch implementation of the Single Shot MultiBox Detector, a well-known object detection architecture introduced in the original SSD paper. It is built to help users train, evaluate, and experiment with object detection models using PyTorch rather than the original Caffe implementation. The repository includes the major components needed for an object detection workflow, including training scripts, evaluation scripts, demos, and utility modules. It supports commonly used benchmark... -
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Liberator
Liberator is a Clojure library for building RESTful applications
Liberator is a Clojure library designed for building RESTful web APIs in a principled, declarative manner. It abstracts HTTP semantics into resource constructs driven by representational state transfer, allowing deep control over HTTP behavior and content negotiation. Liberator used to be known as compojure-rest. It got renamed in July 2012. Liberator is loosely modeled after WebMachine and shares the same aims as Bishop. The examples in this document rely on you installing Leiningen 2. -
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EVMachine
Disigned to be used for School Paarliamentary Election
Disigned to be used for School Paarliamentary Election -
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Scalable Distributed Deep-RL
A TensorFlow implementation of Scalable Distributed Deep-RL
Scalable Agent is the open implementation of IMPALA (Importance Weighted Actor-Learner Architectures), a highly scalable distributed reinforcement learning framework developed by Google DeepMind. IMPALA introduced a new paradigm for efficiently training agents across large-scale environments by decoupling acting and learning processes. In this architecture, multiple actor processes interact with their environments in parallel to collect trajectories, which are then asynchronously sent to a...
