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Type: Organization
Type: Organization
Aika is a new type of artificial neural network designed to more closely mimic the behavior of a biological brain and to bridge the gap to classical AI. A key design decision in the Aika network is to conceptually separate the activations from their neurons, meaning that there are two separate graphs. One graph consisting of neurons and synapses representing the knowledge the network has already acquired and another graph consisting of activations and links describing the information the network was able to infer about a concrete input data set. There is a one-to-many relation between the neurons and the activations. For example, there might be a neuron representing a word or a specific meaning of a word, but there might be several activations of this neuron, each representing an occurrence of this word within the input data set. A consequence of this decision is that we have to give up on the idea of a fixed layered topology for the network, since the sequence in which the activations are fired depends on the input data set. Within the activation network, each activation is grounded within the input data set, even if there are several activations in between. This means links between activations serve two purposes. On the one hand, they are used to sum up the synapse weights and, on the other hand they propagate the identity to higher level activations.
[NO LONGER MAINTAINED] Command-line utility for auto-generating subtitles for any video file
Bypass Paywalls web browser extension for Chrome and Firefox.
Open Source image hosting software
Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.
Theano-based implementation of Deep Semi-NMF.
One-click Install and Configure Dnsmasq and Sniproxy for CentOS/Debian/Ubuntu
Dplyr: A grammar of data manipulation
Lightweight Elasticsearch client for R.
R package for ensemble MCMC for Factorial Hidden Markov Models
Modern desktop RSS reader built with Electron, React, and Fluent UI
层次密度聚类研究(用与用户聚类) 分理论与实际
ImgURL是一个简单、纯粹的图床程序,让个人图床多一个选择。
An implementation of the largeVis algorithm for visualizing large, high-dimensional datasets, for R
利用Github实现自己的图床! Use Github Build Your Own Markdown Picture Server
Code for the experiments in "Sliding Window Approach for Online Energy Disaggregation Using Artificial Neural Networks"
Python Matrix Factorization Module
semi non-negative matrix factorization project
纯kotlin实现, 节点爬取,筛选, 支持Clash,base64订阅解析,定期生成可用的ss, ssr, v2ray, trojan节点
CRF(Conditional Random Field) Layer for TensorFlow 1.X with many powerful functions
Transfer Learning for Non-Intrusive Load Monitoring
写作爱好者的同步工具
🖼 新浪微博图床 Chrome/Firefox 扩展,支持同步到微相册
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.