Search Results for "q learning algorithm" - Page 3
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Showing 241 open source projects for "q learning algorithm"
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ls-recession-indicator
Unemployment rate-based Least Squares Recession Indicator
This project attempts to create a machine learning model which predicts the probability of recession for any given month based on the current month plus the 11 prior months of U.S. U-3 unemployment rate data. It utilizes a linear regression / least squares algorithm. -
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Lumi-HSP
This is an AI language model that can predict Heart failure or stroke
Using thsi AI model, you can predict the chances of heart stroke and heart failure. HIGLIGHTS : 1. Accuracy of this model is 95% 2. This model uses the powerful Machine Learning algorithm "GradientBoosting" for predicting the outcomes. 3. An easy to use model and accessible to everyone. -
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EarQuiz Frequencies
Software for technical ear training on equalization
... process involves ongoing learning and testing yourself. In the Learn mode, you listen to the pink noise or music (or other external audio) excerpts with switched off and on 1-octave or 1/3-octave graphic EQ, boosting or cutting frequency bands within certain spectral ranges. Then in the Test mode you are given a sequence of 10 similar examples, where you try to guess, which frequencies are boosted or cut, and you get scored. The program is Free Software, distributed under GNU GPL v3 License. -
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pwwAutoVision
Non-standard automated vision software
The only software that integrates vision and motion, as well as interfaces and reports, is developed with zero code and comes with online debugging. Link：https://pan.baidu.com/s/1vsTptn_pvtbK2sDhWVCZJg code：1234 -
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Nest-o-Patch
Software for analysis of patch-clamp recordings and other wave data
This program was designed mainly for preconditioning and analysis of electrophysiological data, including patch-clamp and 2-electrode voltage clamp recordings. The program includes tools both for basic analysis of whole-cell recordings or analysis of single channel properties. Program can display and analyse long traces incuding many sweeps, series and even groups simultaneously. Current amplitudes or time intervals are easily measured. Single channel conductance, kinetics, NPo, as well as... -
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EduCDM
The Model Zoo of cognitive diagnosis models
... is a type of model that infers students' knowledge states from their learning behaviors (especially exercise response logs). Typically, the input of a CDM could be the students' response logs of items (i.e., exercises/questions), the Q-matrix that denotes the correlation between items and knowledge concepts (skills). The output is the diagnosed student knowledge states, such as students' abilities and students' proficiencies on each knowledge concepts. -
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MACE
Deep learning inference framework optimized for mobile platforms
Mobile AI Compute Engine (or MACE for short) is a deep learning inference framework optimized for mobile heterogeneous computing on Android, iOS, Linux and Windows devices. Runtime is optimized with NEON, OpenCL and Hexagon, and Winograd algorithm is introduced to speed up convolution operations. The initialization is also optimized to be faster. Chip-dependent power options like big.LITTLE scheduling, Adreno GPU hints are included as advanced APIs. UI responsiveness guarantee is sometimes... -
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codeforces-go
Solutions to Codeforces by Go
Golang algorithm competition template library. Due to the complexity of algorithm knowledge points, it is necessary to classify the algorithms you have learned and the questions you have done. An algorithm template should cover the following points. Basic introduction to the algorithm (core idea, complexity, etc.) Reference links or book chapters (good material) Template code (can contain some comments, usage instructions) Template supplements (extra codes in common question types, modeling... -
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JQM Java Quine McCluskey
JQM - Java Quine McCluskey for minimization of Boolean functions.
Java Quine McCluskey implements the Quine McCluskey algorithm with Petrick’s Method (or the method of prime implicants) for minimization of Boolean functions. This software can be used both for learning and solving real problems. As a learning/teaching tool, it presents not only the results but also how the problem was solved as well as how to use Karnaugh Maps to solve the problem. Up to sixteen functions of sixteen variables can be minimized. A graphical interface is provided for entering... -
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C++ Eigenvectors
C++ matrix class template with eigenvectors
All code is in a single .h file except for a .cpp file with the main() demo. Computation time scales as N^4. Also has class template Polynomial. Has extensive operators and methods. Algorithm summary: Any polynomial of degree m is uniquely and simply determined by its values at m+1 points. In particular the characteristic polynomial of an NxN matrix A can be determined by evaluating the determinant |x * I - A| at N+1 distinct values of x. Householder reduction is used to find... -
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BerryNet
Deep learning gateway on Raspberry Pi and other edge devices
This project turns edge devices such as Raspberry Pi into an intelligent gateway with deep learning running on it. No internet connection is required, everything is done locally on the edge device itself. Further, multiple edge devices can create a distributed AIoT network. At DT42, we believe that bringing deep learning to edge devices is the trend towards the future. It not only saves costs of data transmission and storage but also makes devices able to respond according to the events shown... -
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Anime4KCPP
A high performance anime upscaler
Anime4KCPP provides an optimized bloc97's Anime4K algorithm version 0.9, and it also provides its own CNN algorithm ACNet, it provides a variety of way to use, including preprocessing and real-time playback, it aims to be a high-performance tool to process both image and video. This project is for learning and the exploration task of the algorithm course in SWJTU. Anime4K is a simple high-quality anime upscale algorithm. Version 0.9 does not use any machine learning approaches and can be very... -
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PaddlePaddle models
Pre-trained and Reproduced Deep Learning Models
Pre-trained and Reproduced Deep Learning Models ("Flying Paddle" official model library, including a variety of academic frontier and industrial scene verification of deep learning models) Flying Paddle's industrial-level model library includes a large number of mainstream models that have been polished by industrial practice for a long time and models that have won championships in international competitions; it provides many scenarios for semantic understanding, image classification, target... -
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ML.NET Samples
Samples for ML.NET, an open source and cross-platform machine learning
ML.NET is a cross-platform open-source machine learning framework that makes machine learning accessible to .NET developers. In this GitHub repo, we provide samples that will help you get started with ML.NET and how to infuse ML into existing and new .NET apps. We're working on simplifying ML.NET usage with additional technologies that automate the creation of the model for you so you don't need to write the code by yourself to train a model, you simply need to provide your datasets. The "best... -
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LaPath
Learning Automata algorithm for the shortest path problem.
The shortest path problem is solved by many methods. Heuristics offer lower complexity in expense of accuracy. There are many use cases where the lower accuracy is acceptable in return of lower consumption of computing resources. Learning Automata (LA) are adaptive mechanisms requiring feedback from the executing environment to converge to certain states. In the context of network routing, LA residing at intermediate nodes along a path, exploit feedback from the destination node for reducing... -
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AmPEP and AxPEP
Sequence-based Antimicrobial Peptide Prediction by Random Forest
Antimicrobial peptides (AMPs) are promising candidates in the fight against multidrug-resistant pathogens due to its broad range of activities and low toxicity. However, identification of AMPs through wet-lab experiment is still expensive and time consuming. AmPEP is an accurate computational method for AMP prediction using the random forest algorithm. The prediction model is based on the distribution patterns of amino acid properties along the sequence. Our optimal model, AmPEP with 1:3 data... -
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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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Computer Vision Pretrained Models
A collection of computer vision pre-trained models
A pre-trained model is a model created by someone else to solve a similar problem. Instead of building a model from scratch to solve a similar problem, we can use the model trained on other problem as a starting point. A pre-trained model may not be 100% accurate in your application. For example, if you want to build a self-learning car. You can spend years building a decent image recognition algorithm from scratch or you can take the inception model (a pre-trained model) from Google which... -
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Forecasting Best Practices
Time Series Forecasting Best Practices & Examples
Time series forecasting is one of the most important topics in data science. Almost every business needs to predict the future in order to make better decisions and allocate resources more effectively. This repository provides examples and best practice guidelines for building forecasting solutions. The goal of this repository is to build a comprehensive set of tools and examples that leverage recent advances in forecasting algorithms to build solutions and operationalize them. Rather than... -
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Simulation on energy management of an uneven clustered EH-WSNs by cooperative q-learning Power management in wireless sensor networks (WSNs) is very important due to the limited energy of batteries. Sensor nodes with harvesters can extract energy from environmental sources as supplemental energy to break this limitation. In a clustered solar-powered sensor network where nodes in the network are grouped into clusters, data collected by cluster members are sent to their cluster head...
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java-string-similarity
Implementation of various string similarity and distance algorithms
Implementation of various string similarity and distance algorithms: Levenshtein, Jaro-winkler, n-Gram, Q-Gram, Jaccard index, Longest Common Subsequence edit distance, cosine similarity. A library implementing different string similarity and distance measures. A dozen of algorithms (including Levenshtein edit distance and sibblings, Jaro-Winkler, Longest Common Subsequence, cosine similarity etc.) are currently implemented. The main characteristics of each implemented algorithm are presented... -
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jieba
Stuttering Chinese word segmentation
... for word segmentation in search engines. The paddle mode uses the PaddlePaddle deep learning framework to train the sequence labeling (bidirectional GRU) network model to achieve word segmentation. Also supports part-of-speech tagging. To use paddle mode, you need to install paddlepaddle-tiny, pip install paddlepaddle-tiny==1.6.1. Currently paddle mode supports jieba v0.40 and above. For versions below jieba v0.40, please upgrade jieba, pip install jieba --upgrade. -
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ViralPlaque
A fast, open-source and versatile ImageJ macro for the automated dete
Plaque assay has been used for a long time to determine infectious titers. We present ViralPlaque, a fast, open-source and versatile ImageJ macro for the automated determination of viral plaque dimensions from digital images. Also, a machine learning plugin is integrated in the analysis algorithm for adaptation of ViralPlaque to the user’s needs and experimental conditions. A high correlation between manual and automated measurements of plaque dimensions was demonstrated. This macro... -
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RecNN
Reinforced Recommendation toolkit built around pytorch 1.7
This is my school project. It focuses on Reinforcement Learning for personalized news recommendation. The main distinction is that it tries to solve online off-policy learning with dynamically generated item embeddings. I want to create a library with SOTA algorithms for reinforcement learning recommendation, providing the level of abstraction you like. -
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Flip card learning program for iPhone with a spaced learning algorithm. Create your own databases and edit the cards directly on the iPhone. Import Palm databases or csv-formatted files and backup your data with our Java application.