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limdu - Machine-learning for Node.js
Limdu is a machine-learning framework for Node.js. It supports multi-label classification, online learning, and real-time classification. Therefore, it is especially suited for natural language understanding in dialog systems and chat-bots.Limdu is in an "alpha" state - some parts are working (see this readme), but some parts are missing or not tested. Contributions are welcome.
classifier classification categorization text-classification natural-lanaguage-understanding machine-learning multi-label multilabel multi-class multiclass online-learning naive-bayes winnow perceptron svm linear-svm binary-relevance one-vs-allHanLP - 自然语言处理 中文分词 词性标注 命名实体识别 依存句法分析 关键词提取 新词发现 短语提取 自动摘要 文本分类 拼音简繁
汉语言处理包
nlp natural-language-processing hanlp crf hmm trie textrank doublearraytrie neural-network chinese-word-segmentation text-mining pos-tagging dependency-parser text-classification word2vec perceptron named-entity-recognition text-clusteringmachine_learning_basics - Plain python implementations of basic machine learning algorithms
This repository contains implementations of basic machine learning algorithms in plain Python (Python Version 3.6+). All algorithms are implemented from scratch without using additional machine learning libraries. The intention of these notebooks is to provide a basic understanding of the algorithms and their underlying structure, not to provide the most efficient implementations. After several requests I started preparing notebooks on how to preprocess datasets for machine learning. Within the next months I will add one notebook for each kind of dataset (text, images, ...). As before, the intention of these notebooks is to provide a basic understanding of the preprocessing steps, not to provide the most efficient implementations.
machine-learning logistic-regression ipynb machine-learning-algorithms linear-regression perceptron python-implementations kmeans algorithm python3 neural-network k-nearest-neighbours k-nearest-neighbor k-nn neural-networkssynaptic - architecture-free neural network library for node.js and the browser
Synaptic is a javascript neural network library for node.js and the browser, its generalized algorithm is architecture-free, so you can build and train basically any type of first order or even second order neural network architectures. This library includes a few built-in architectures like multilayer perceptrons, multilayer long-short term memory networks (LSTM), liquid state machines or Hopfield networks, and a trainer capable of training any given network, which includes built-in training tasks/tests like solving an XOR, completing a Distracted Sequence Recall task or an Embedded Reber Grammar test, so you can easily test and compare the performance of different architectures.
neural-network machine-learning long-short-term-memory perceptron architecture-freetalisman - A straightforward & modular NLP, machine learning & fuzzy matching library for JavaScript
The library's full documentation can be found here. Be sure to lint & pass the unit tests before submitting your pull request.
natural-language-processing machine-learning fuzzy-matching clustering record-linkage bayes bloom-filter canberra caverphone chebyshev cologne cosine classifier daitch-mokotoff dice fingerprint fuzzy hamming k-means jaccard jaro lancaster levenshtein lig metaphone mra ngrams nlp nysiis perceptron phonetic porter punkt schinke sorensen soundex stats tfidf tokenizer tversky vectorizer winklerperceptron - a perceptron
This is an implementation of the perceptron learning algorithm for node.js. The perceptron starts with random weights if you don't provide any defaults. Weights are adjusted according to the delta rule each time you call train and the current weights give the wrong answer. Since this adjustment can cause the perceptron to 'unlearn' previously learned inputs, retrain iterates over all previous inputs, calling train again. Both train and retrain return a boolean success value, indicating if the input(s) were learned.
machine-learning perceptronrosenblattperceptronjs - Training of a Perceptron in the Browser with Vue.js & SVG.
This is a demonstration of training a perceptron to categorize entities between friendly or not according to 2 features, teeth and size. And then install dependencies.
vuejs perceptron machine-learning svg webpacksnakeneuralnetworkjs - Neuroevolution of Neural Network for snakes in the Browser
This is a demonstration of evolving a neural network thanks to genetics algorithms in the browser using a multilayer perceptron (150-15-15-1). The initial population contains 36 individuals, each assigned a different genome. They will fight following a round-robin tournament. At the end the top 7 are kept alive, and the remaining 29 are created by breeding from the 7.
neural-network genetic-algorithm machine-learning neural synaptic perceptron neurralnetworknode-ml - A Collection of Machine Learning algorithms built for use with NodeJS
( Development is current suspended ). Models are acted upon by the perceive or predict functions. These functions currently do the same thing, the wording is indicative of the nature of the result, and the action the model has taken on the data.
machine learning ml classifier clustering bayes k-means logistic regression perceptron neural netcl-online-learning - A collection of machine learning algorithms for online linear classification written in Common Lisp
A collection of machine learning algorithms for online linear classification written in Common Lisp. cl-online-learning is available from Quicklisp.
common-lisp machine-learning perceptron classifierNNet - algorithm for study: multi-layer-perceptron, cluster-graph, cnn, rnn, restricted boltzmann machine, bayesian network
algorithm for study: multi-layer-perceptron, cluster-graph, cnn, rnn, restricted boltzmann machine, bayesian network
course algorithm clustering cplusplus graph cluster-computing multilayer-perceptron-network perceptron cnn clustering-graph study restricted-boltzmann-machine bayesian-networktensorbag - Collection of tensorflow notebooks tutorials for implementing the most important Deep Learning algorithms
Tensorbag is a collection of tensorflow tutorial on different Deep Learning and Machine Learning algorithms. The tutorials are organised as jupyter notebooks and require tensorflow >= 1.5. There is a subset of notebooks identified with the tag [quiz] that directly ask to the reader to complete part of the code. In the same folder there is always a complementary notebook with the complete solution.
cifar-10 generative-adversarial-networks mnist lenet-5 convolutional-neural-networks resnet-18 notebook deep-learning tensorflow resnet tfrecord-format cifar-100 tensorflow-tutorials kmeans-clustering perceptron autoencoder tutorialDeep-Learning-From-Scratch - Six snippets of code that made deep learning what it is today.
There are six snippets of code that made deep learning what it is today. Coding the History of Deep Learning on Floydhub' s blog covers the inventors and the background to their breakthroughs. In this repo, you can find all the code samples from the story.
deep-learning linear-regression mnist perceptron least-squares gradient-descent backpropagationVaris - Golang Neural Network
Some time ago I decided to learn Go language and neural networks. So it's my variation of Neural Networks library. I tried to make library for programmers (not for mathematics). For now Varis is 0.1 version.
neural-network machine-learning perceptron
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