AiLearning - AiLearning: 机器学习 - MachineLearning - ML、深度学习 - DeepLearning - DL、自然语言处理 NLP

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AiLearning: 机器学习 - MachineLearning - ML、深度学习 - DeepLearning - DL、自然语言处理 NLP



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rumale - Rumale is a machine learning library in Ruby

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Rumale (Ruby machine learning) is a machine learning library in Ruby. Rumale provides machine learning algorithms with interfaces similar to Scikit-Learn in Python. Rumale supports Linear / Kernel Support Vector Machine, Logistic Regression, Linear Regression, Ridge, Lasso, Kernel Ridge, Factorization Machine, Naive Bayes, Decision Tree, AdaBoost, Gradient Tree Boosting, Random Forest, Extra-Trees, K-nearest neighbor classifier, K-Means, K-Medoids, Gaussian Mixture Model, DBSCAN, SNN, Power Iteration Clustering, Mutidimensional Scaling, t-SNE, Principal Component Analysis, Kernel PCA and Non-negative Matrix Factorization. This project was formerly known as "SVMKit". If you are using SVMKit, please install Rumale and replace SVMKit constants with Rumale.

tensorflow_cookbook - Code for Tensorflow Machine Learning Cookbook

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This chapter intends to introduce the main objects and concepts in TensorFlow. We also introduce how to access the data for the rest of the book and provide additional resources for learning about TensorFlow. After we have established the basic objects and methods in TensorFlow, we now want to establish the components that make up TensorFlow algorithms. We start by introducing computational graphs, and then move to loss functions and back propagation. We end with creating a simple classifier and then show an example of evaluating regression and classification algorithms.

python-fp-growth - An implementation of the FP-growth algorithm in pure Python.

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This module provides a pure Python implementation of the FP-growth algorithm for finding frequent itemsets. FP-growth exploits an (often-valid) assumption that many transactions will have items in common to build a prefix tree. If the assumption holds true, this tree produces a compact representation of the actual transactions and is used to generate itemsets much faster than Apriori can. Note that find_frequent_itemsets returns a generator of itemsets, not a greedily-populated list. Each item must be hashable (i.e., it must be valid as a member of a dictionary or a set).

python-machine-learning-book - The "Python Machine Learning (1st edition)" book code repository and info resource

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This GitHub repository contains the code examples of the 1st Edition of Python Machine Learning book. If you are looking for the code examples of the 2nd Edition, please refer to this repository instead. What you can expect are 400 pages rich in useful material just about everything you need to know to get started with machine learning ... from theory to the actual code that you can directly put into action! This is not yet just another "this is how scikit-learn works" book. I aim to explain all the underlying concepts, tell you everything you need to know in terms of best practices and caveats, and we will put those concepts into action mainly using NumPy, scikit-learn, and Theano.

sklearn2pmml - Python library for converting Scikit-Learn pipelines to PMML

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Python library for converting Scikit-Learn pipelines to PMML. This library is a thin wrapper around the JPMML-SkLearn command-line application. For a list of supported Scikit-Learn Estimator and Transformer types, please refer to the documentation of the JPMML-SkLearn project.

sklearn-evaluation - scikit-learn model evaluation made easy: plots, tables and markdown reports.

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scikit-learn model evaluation made easy: plots, tables and markdown reports. Works with Python 2 and 3.

mkl-dnn - Intel(R) Math Kernel Library for Deep Neural Networks (Intel(R) MKL-DNN)

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Intel MKL-DNN repository migrated to The old address will continue to be available and will redirect to the new repo. Please update your links. Intel(R) Math Kernel Library for Deep Neural Networks (Intel(R) MKL-DNN) is an open source performance library for deep learning applications. The library accelerates deep learning applications and framework on Intel(R) architecture. Intel(R) MKL-DNN contains vectorized and threaded building blocks which you can use to implement deep neural networks (DNN) with C and C++ interfaces.

gplearn - Genetic Programming in Python, with a scikit-learn inspired API

  •    Python

gplearn implements Genetic Programming in Python, with a scikit-learn inspired and compatible API. While Genetic Programming (GP) can be used to perform a very wide variety of tasks, gplearn is purposefully constrained to solving symbolic regression problems. This is motivated by the scikit-learn ethos, of having powerful estimators that are straight-forward to implement.

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Mailgun library to extract message quotations and signatures.For machine learning talon currently uses the scikit-learn library to build SVM classifiers. The core of machine learning algorithm lays in talon.signature.learning package. It defines a set of features to apply to a message (, how data sets are built (, classifier’s interface (

NCRFpp - NCRF++, an Open-source Neural Sequence Labeling Toolkit

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Sequence labeling models are quite popular in many NLP tasks, such as Named Entity Recognition (NER), part-of-speech (POS) tagging and word segmentation. State-of-the-art sequence labeling models mostly utilize the CRF structure with input word features. LSTM (or bidirectional LSTM) is a popular deep learning based feature extractor in sequence labeling task. And CNN can also be used due to faster computation. Besides, features within word are also useful to represent word, which can be captured by character LSTM or character CNN structure or human-defined neural features. NCRF++ is a PyTorch based framework with flexiable choices of input features and output structures. The design of neural sequence labeling models with NCRF++ is fully configurable through a configuration file, which does not require any code work. NCRF++ is a neural version of CRF++, which is a famous statistical CRF framework.

hands_on_Ml_with_Sklearn_and_TF - OReilly Hands On Machine Learning with Scikit Learn and TensorFlow (Sklearn与TensorFlow机器学习实用指南)

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OReilly Hands On Machine Learning with Scikit Learn and TensorFlow (Sklearn与TensorFlow机器学习实用指南)

Hands-On-Deep-Learning-Algorithms-with-Python - Master Deep Learning Algorithms with Extensive Math by Implementing them using TensorFlow

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Deep learning is one of the most popular domains in the artificial intelligence (AI) space, which allows you to develop multi-layered models of varying complexities. This book is designed to help you grasp things, from basic deep learning algorithms to the more advanced algorithms. The book is designed in a way that first you will understand the algorithm intuitively, once you have a basic understanding of the algorithms, then you will master the underlying math behind them effortlessly and then you will learn how to implement them using TensorFlow step by step. The book covers almost all the state of the art deep learning algorithms. First, you will get a good understanding of the fundamentals of neural networks and several variants of gradient descent algorithms. Later, you will explore RNN, Bidirectional RNN, LSTM, GRU, seq2seq, CNN, capsule nets and more. Then, you will master GAN and various types of GANs and several different autoencoders.

scikit-learn-doc-zh - scikit-learn(sklearn) 官方文档中文版

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工欲善其事, 必先利其器 ... 工具随意, 能达到效果就好. 我这里使用的是 VSCode 编辑器. 简易的使用指南请参阅: VSCode Windows 平台入门使用指南, 介绍了 VSCode 与 github 一起搭配的简易使用的方法. 如果要将 VSCode 的 Markdown 预览风格切换为 github 的风格,请参阅: VSCode 修改 markdown 的预览风格为 github 的风格. 注意注意注意: 为了尽量正规化各顶级项目的翻译,更便于以后的迭代更新,我们在 scikit-learn 文档翻译中使用了 Git 的分支,具体应用方法请参阅: 使用 Git 分支进行迭代翻译.

hyperopt-sklearn - Hyper-parameter optimization for sklearn

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Hyperopt-sklearn is Hyperopt-based model selection among machine learning algorithms in scikit-learn. If you are familiar with sklearn, adding the hyperparameter search with hyperopt-sklearn is only a one line change from the standard pipeline.

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