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TensorFlow-Book - Accompanying source code for Machine Learning with TensorFlow
This is the official code repository for Machine Learning with TensorFlow. Get started with machine learning using TensorFlow, Google's latest and greatest machine learning library.
tensorflow machine-learning regression convolutional-neural-networks logistic-regression book reinforcement-learning autoencoder linear-regression classification clusteringNakedTensor - Bare bone examples of machine learning in TensorFlow
This is a bare bones example of TensorFlow, a machine learning package published by Google. You will not find a simpler introduction to it. In each example, a straight line is fit to some data. Values for the slope and y-intercept of the line that best fit the data are determined using gradient descent. If you do not know about gradient descent, check out the Wikipedia page.
tensorflow tensorflow-tutorials distributed-computing simple big-data linear-regression tensorflow-examples tensorflow-exercisesDAT8 - General Assembly's 2015 Data Science course in Washington, DC
Course materials for General Assembly's Data Science course in Washington, DC (8/18/15 - 10/29/15).
data-science machine-learning scikit-learn data-analysis pandas jupyter-notebook course linear-regression logistic-regression model-evaluation naive-bayes natural-language-processing decision-trees ensemble-learning clustering regular-expressions web-scraping data-visualization data-cleaningtensorflow_cookbook - Code for Tensorflow Machine Learning Cookbook
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.
tensorflow tensorflow-cookbook linear-regression neural-network tensorflow-algorithms rnn cnn svm nlp packtpub machine-learning tensorboard classification regression kmeans-clustering genetic-algorithm odemachine_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-networksMachine-Learning-with-Python - Python codes for common Machine Learning Algorithms
Python codes for common Machine Learning Algorithms
linear-regression polynomial-regression logistic-regression decision-trees random-forest svm svr knn-classification naive-bayes-classifier kmeans-clustering hierarchical-clustering pca lda xgboost-algorithmgrt - gesture recognition toolkit
The Gesture Recognition Toolkit (GRT) is a cross-platform, open-source, C++ machine learning library designed for real-time gesture recognition. Classification: Adaboost, Decision Tree, Dynamic Time Warping, Gaussian Mixture Models, Hidden Markov Models, k-nearest neighbor, Naive Bayes, Random Forests, Support Vector Machine, Softmax, and more...
gesture-recognition grt machine-learning gesture-recognition-toolkit support-vector-machine random-forest kmeans dynamic-time-warping softmax linear-regressionhighcharts_trendline - HighCharts demo of scatter plot, including a trend line
This project illustrates how you can easily add a trendline to your Highchart charts. Note: If you are looking for a more complete implementation, checkout the highcharts-regression plugin.
linear-regression highcharts-regression highcharts-trendline highcharts plot charthdnom - Benchmarking and Visualization Toolkit for Penalized Cox Models
hdnom creates nomogram visualizations for penalized Cox regression models, with the support of reproducible survival model building, validation, calibration, and comparison for high-dimensional data. Browse the vignettes to start.
high-dimensional-data survival-analysis benchmark penalized-cox-models linear-regression nomogram-visualizationml - A set of machine learning experiments in Clojure
A set of machine learning experiments in Clojure
logistic-regression regularization linear-regressionPolynomialRegression
PolynomialRegression.php packaged for composer.
polynomial-regression linear-regressioninteractive-simple-linear-regression - A PureScript, browser-based implementation of simple linear regression
Input 2D data points and fit a simple linear regression model using gradient descent. Built with PureScript. Playable at lettier.com/simple-linear-regression/. For a full write up, visit Let's make a Linear Regression Calculator with PureScript.
linear-regression gradient-descent data-science purescript functional-programming press-statistic machine-learning machine-learning-algorithms regression functional artificial-intelligence ai statistics web-development halogen nueral-networks purescript-halogen frontendlinearReg.js - Linear regression with Gradient descent package for NPM.
A javascript implementation of linear regression. Loosely based on Prof. Ng's MOOC on Machine Learning.
linear-regression machine-learning linearregression gradientdescent machinelearningmsaenet - Multi-Step Adaptive Estimation Methods for Reducing False Positive Selection in Sparse Regressions
msaenet implements the multi-step adaptive elastic-net (MSAENet) algorithm for feature selection in high-dimensional regressions proposed in Xiao and Xu (2015) <DOI:10.1080/00949655.2015.1016944> (PDF). Nonconvex multi-step adaptive estimations based on MCP-net or SCAD-net are also supported.
high-dimensional-data variable-selection linear-regression machine-learning false-positive-controlClassifierToolbox - A MATLAB toolbox for face classifier 1.0.7
This package provides various tools for classification, e.g., image classification, face recogntion, and related applicaitons. Run run_me_first for path configurations.
face-recognition classification classification-algorithims covariance-matrix sparse-coding linear-regression linear-discriminant-analysis principal-component-analysis symmetric-positive-definite spd subspace manifold matlab-toolbox dictionary-learning manifold-optimization support-vector-machines src eigenfaces pcaGDLibrary - Matlab library for gradient descent algorithms: Version 1.0.1
The GDLibrary is a pure-Matlab library of a collection of unconstrained optimization algorithms. This solves an unconstrained minimization problem of the form, min f(x). Note that the SGDLibrary internally contains this GDLibrary.
optimization optimization-algorithms machine-learning machine-learning-algorithms big-data gradient-descent gradient logistic-regression newton linear-regression svm lasso matrix-completion rosenbrock-problem softmax-regression multinomial-regression statistical-learning classificationSGDLibrary - MATLAB library for stochastic optimization algorithms: Version 1.0.17
The SGDLibrary is a pure-MATLAB library of a collection of stochastic optimization algorithms. This solves an unconstrained minimization problem of the form, min f(x) = sum_i f_i(x). The SGDLibrary is also operable on GNU Octave (Free software compatible with many MATLAB scripts). Note that this SGDLibrary internally contains the GDLibrary.
optimization optimization-algorithms machine-learning machine-learning-algorithms stochastic-optimization-algorithms stochastic-gradient-descent big-data gradient-descent-algorithm gradient logistic-regression sgd variance-reduction newtons-method linear-regression classification online-learning quasi-newtonDataScience350 - Notes for Data Science 350 Class
Notes for Data Science 350 Class
teaching-materials data-science linear-regression genetic-algorithm r bayesian-methods neural-network hypothesis-testingregression - Multivariable regression library in Go
Import the package, create a regression and add data to it. You can use as many variables as you like, in the below example there are 3 variables for each observation. Note: You can also add data points one by one.
regression linear-regressionlinear-regression - Linear regression implemented in Ruby.
An implementation of a linear regression machine learning algorithm implemented in Ruby. More details about this example implementation can be found in this blog post.
linear-regression gradient-descent machine-learning rubyml
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