Search Results for "q-learning" - Page 5
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Showing 229 open source projects for "q-learning"
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Save hundreds of developer hours with components built for SaaS applications.Whether you want full self-service analytics or simpler multi-tenant security, Qrvey’s embeddable components and scalable data management remove the guess work.
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Top-Rated Free CRM SoftwareHubSpot is an AI-powered customer platform with all the software, integrations, and resources you need to connect your marketing, sales, and customer service. HubSpot's connected platform enables you to grow your business faster by focusing on what matters most: your customers.
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SGraph
Create charts by retrieving data from Stata on-the-fly
SGraph is a web application that interacts with Stata for creating graphs. It is a demo application that demostrates how to exploit SWire for creating web applications that communicate with Stata. SGraph is an open source project and you can download the code from the project web site web site or try the app on-line here: http://sgraph-with-swire.sourceforge.net/ The source project is hosted in GitHub: https://github.com/lomagno/sgraph -
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Reliquarium
3D slider puzzles for kids; helps teach solid geometry
... the cursor rotates the cube for a better view angle. The mouse wheel zooms. Typing a number 1..8 selects a block to move, 0 selects the relic itself. Then use the keys {u,d,l,r,f,b} to move it. The proper command to extract the archive and maintain the directory structure is "7z x filename". These unique puzzles are spatially challenging, yet easy enough for children and will likely help with learning 3D visualization, geometry, problem solving and computer skills. -
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ManifoldLearning
Package for manifold learning and nonlinear dimensionality reduction
A Julia package for manifold learning and nonlinear dimensionality reduction. Most of the methods use k-nearest neighbors method for constructing local subspace representation. By default, neighbors are computed from a distance matrix of a dataset. This is not an efficient method, especially, for large datasets. -
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AWS Step Functions Data Science SDK
For building machine learning (ML) workflows and pipelines on AWS
The AWS Step Functions Data Science SDK is an open-source library that allows data scientists to easily create workflows that process and publish machine learning models using Amazon SageMaker and AWS Step Functions. You can create machine learning workflows in Python that orchestrate AWS infrastructure at scale, without having to provision and integrate the AWS services separately. The best way to quickly review how the AWS Step Functions Data Science SDK works is to review the related example... -
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Face Mask Detection
Face Mask Detection system based on computer vision and deep learning
Face Mask Detection system based on computer vision and deep learning using OpenCV and Tensorflow/Keras. Face Mask Detection System built with OpenCV, Keras/TensorFlow using Deep Learning and Computer Vision concepts in order to detect face masks in static images as well as in real-time video streams. Amid the ongoing COVID-19 pandemic, there are no efficient face mask detection applications which are now in high demand for transportation means, densely populated areas, residential districts... -
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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. -
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EZStacking
EZStacking is Jupyter notebook generator for machine learning
EZStacking is Jupyter notebook generator for supervised learning problems using Scikit-Learn pipelines and stacked generalization. EZStacking handles classification and regression problems for structured data. It can also be viewed as a development tool, because a notebook generated with EZStacking contains: -an exploratory data analysis (EDA) used to assess data quality - a modelling producing a reduced-size stacked estimator - a server returning a prediction, a measure of the quality... -
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ReinforcementLearningAnIntroduction.jl
Julia code for the book Reinforcement Learning An Introduction
This project provides the Julia code to generate figures in the book Reinforcement Learning: An Introduction(2nd). One of our main goals is to help users understand the basic concepts of reinforcement learning from an engineer's perspective. Once you have grasped how different components are organized, you're ready to explore a wide variety of modern deep reinforcement learning algorithms in ReinforcementLearningZoo.jl. -
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SciMLTutorials.jl
Tutorials for doing scientific machine learning (SciML)
SciMLTutorials.jl holds PDFs, webpages, and interactive Jupyter notebooks showing how to utilize the software in the SciML Scientific Machine Learning ecosystem. This set of tutorials was made to complement the documentation and the devdocs by providing practical examples of the concepts. For more details, please consult the docs. To view the SciML Tutorials, go to tutorials.sciml.ai. By default, this will lead to the latest tagged version of the tutorials -
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Team Maker is software that helps instructors quickly create cooperative learning teams. It works by surveying students based on a set of questions the instructor picks, and then builds teams based on these student responses.
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Zenoss Community Edition
Zenoss - Intelligent IT Operations Management
Zenoss provides software-defined IT operations for the world’s largest organizations. We deliver the ultimate level of IT service health with simplicity by providing the most granular and intelligent IT service modeling possible, at any scale, and sharing these unique insights with other IT operations management (ITOM) tools to make them more efficient. Zenoss Community Edition is not a “demo” or trial version of Zenoss Enterprise or Zenoss Cloud! Before You install Zenoss Community... -
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Through NSF funding and a collaboration with the Smithsonian Latino Center, the project augments the learning environment of middle school students through the creation of a computer game based on regional issues explored through information overload, virtual simulations, and real world data. The game has a theme of water sustainability and stewardship with an emphasis on information analysis, negotiation, and decision-making. The game immerses students in themes concerning biodiversity...
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ML workspace
All-in-one web-based IDE specialized for machine learning
All-in-one web-based development environment for machine learning. The ML workspace is an all-in-one web-based IDE specialized for machine learning and data science. It is simple to deploy and gets you started within minutes to productively built ML solutions on your own machines. This workspace is the ultimate tool for developers preloaded with a variety of popular data science libraries (e.g., Tensorflow, PyTorch, Keras, Sklearn) and dev tools (e.g., Jupyter, VS Code, Tensorboard) perfectly... -
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Chess.jl
Julia chess programming library
A Julia chess programming library. This package contains various utilities for computer chess programming. There are functions for creating and manipulating chess games, chess positions and sets of squares on the board, for reading and writing chess games in the popular PGN format (including support for comments and variations), for creating opening trees, and for interacting with UCI chess engines. The library was designed for the purpose of doing machine learning experiments in computer chess... -
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MLDataUtils.jl
Utility package for generating, loading, and processing ML datasets
This package is designed to be the end-user facing front-end to all the data related functionality that is spread out across the JuliaML ecosystem. Most of the following sub-categories are covered by a single back-end package that is specialized on that specific problem. Consequently, if one of the following topics is of special interest to you, make sure to check out the corresponding documentation of that package. -
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Gabedit is a Graphical User Interface for FireFly (PC-Gamess), Gamess-US, Gaussian, Molcas, Molpro, MPQC, NWChem, OpenMopac, Orca, PSI4 and Q-Chem computational chemistry packages.
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Web based cataloging and dedupe application. Highly optimized for processing journal articles. Reads MarcXML and dedupes records using the field 773 combined with a fuzzy search on the title. Written for bibnet.org
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cxmooc-tools
Fully automatic tasks, video double-speed seconds
A Superstar (Learning Pass) / Wisdom Tree (Knowing) / Chinese University mooc learning tools, Firefox, Google, Oil Monkey support. Fully automatic tasks, video double-speed seconds, homework exam question bank, automatic verification code coding. After the 2.1 version, it began to support the wisdom tree, and after the 2.2 version, it began to support the mooc name of Chinese universities. This project is completely open source, free, and only for technical learning and communication... -
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Amazon SageMaker Examples
Jupyter notebooks that demonstrate how to build models using SageMaker
Welcome to Amazon SageMaker. This projects highlights example Jupyter notebooks for a variety of machine learning use cases that you can run in SageMaker. If you’re new to SageMaker we recommend starting with more feature-rich SageMaker Studio. It uses the familiar JupyterLab interface and has seamless integration with a variety of deep learning and data science environments and scalable compute resources for training, inference, and other ML operations. Studio offers teams and companies easy... -
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CNN Explainer
Learning Convolutional Neural Networks with Interactive Visualization
In machine learning, a classifier assigns a class label to a data point. For example, an image classifier produces a class label (e.g, bird, plane) for what objects exist within an image. A convolutional neural network, or CNN for short, is a type of classifier, which excels at solving this problem! A CNN is a neural network: an algorithm used to recognize patterns in data. Neural Networks in general are composed of a collection of neurons that are organized in layers, each with their own... -
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StellarGraph
Machine Learning on Graphs
StellarGraph is a Python library for machine learning on graphs and networks. The StellarGraph library offers state-of-the-art algorithms for graph machine learning, making it easy to discover patterns and answer questions about graph-structured data. It can solve many machine learning tasks. Graph-structured data represent entities as nodes (or vertices) and relationships between them as edges (or links), and can include data associated with either as attributes. For example, a graph can... -
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SageMaker Containers
Create SageMaker-compatible Docker containers
Amazon SageMaker is a fully managed service for data science and machine learning (ML) workflows. You can use Amazon SageMaker to simplify the process of building, training, and deploying ML models. To train a model, you can include your training script and dependencies in a Docker container that runs your training code. A container provides an effectively isolated environment, ensuring a consistent runtime and reliable training process. The SageMaker Training Toolkit can be easily added to any... -
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vue-element-admin
A magical vue admin
... is based on ES2015+, vue, vuex, vue-router, vue-cli, axios and element-ui, all requested data is simulated using Mock.js. Understanding and learning this knowledge in advance will greatly help the use of this project. This project integrates a lot of features that you may not use, it will cause a lot of code redundancy. If your project does not pay attention to this issue, you can also directly develop it based on it. Otherwise, you can use vue-admin-template. -
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Deep Learning with PyTorch
Latest techniques in deep learning and representation learning
This course concerns the latest techniques in deep learning and representation learning, focusing on supervised and unsupervised deep learning, embedding methods, metric learning, convolutional and recurrent nets, with applications to computer vision, natural language understanding, and speech recognition. The prerequisites include DS-GA 1001 Intro to Data Science or a graduate-level machine learning course. To be able to follow the exercises, you are going to need a laptop with Miniconda... -
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intelligent thinking system solving
An approach where computer solves problems like a human brain does;
The intelligent, thinking systems solving project uses a method whereby the computer solves the problem in a way a biological way does. The result of this is that it can learn and emulate a behavior or strategy. Two examples of the process use the game of chess and the game of go.
