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The Accord.NET project provides machine learning, statistics, artificial intelligence, computer vision and image processing methods to .NET. It can be used on Microsoft Windows, Xamarin, Unity3D, Windows Store applications, Linux or mobile.

machine-learning framework c-sharp nuget visual-studio statistics unity3d neural-network support-vector-machines computer-vision image-processing ffmpegUsing stock historical data, train a supervised learning algorithm with any combination of financial indicators. Rapidly backtest your model for accuracy and simulate investment portfolio performance.During the testing period, the model signals to buy or sell based on its prediction for price movement the following day. By putting your trading algorithm aside and testing for signal accuracy alone, you can rapidly build and test more reliable models.

machine-learning support-vector-machines portfolio-simulation backtesting-trading-strategies stock-marketsvmjs is a lightweight implementation of the SMO algorithm to train a binary Support Vector Machine. As this uses the dual formulation, it also supports arbitrary kernels. Correctness test, together with MATLAB reference code are in /test. Corresponding code is inside /demo directory.

support-vector-machines machine-learning classifier svmTraining AI machine learning models on the Fashion MNIST dataset. Fashion-MNIST is a dataset consisting of 70,000 images (60k training and 10k test) of clothing objects, such as shirts, pants, shoes, and more. Each example is a 28x28 grayscale image, associated with a label from 10 classes. The 10 classes are listed below.

mnist fashion dataset fashion-mnist machine-learning artificial-intelligence artificial-neural-networks support-vector-machines svm xgboost data-science r supervised-learning classification image-recognition image-classificationThis 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 pcaThe SparseGDLibrary is a pure-Matlab library of a collection of unconstrained optimization algorithms for sparse modeling. Run run_me_first for path configurations.

optimization optimization-algorithms machine-learning-algorithms machine-learning big-data gradient-descent sparse-linear-solver sparse-regression lasso-regression lasso elasticnet solver algorithms admm proximal-algorithms proximal-operators logistic-regression matrix-completion coordinate-descent support-vector-machinesSpam filtering module with Machine Learning using SVM. spampy is a classifier that uses Support Vector Machines which tries to classify given raw emails if they are spam or not. Support vector machines (SVMs) are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis. Given a set of training examples, each marked as belonging to one or the other of two categories, an SVM training algorithm builds a model that assigns new examples to one category or the other, making it a non-probabilistic binary linear classifier.

machine-learning support-vector-machines sklearn sklearn-classify numpy scipy spam-classification enron-spam-datasetThis example project demonstrates how support vector machine (SVM) may be used to solve a classification problem (spam filter) in JavaScript. The SMS Spam Collection Dataset from kaggle is used for the purpose of training and testing the algorithm. Before training the algorithm, the data set is prepared with common practices to finally extract a feature vector for each SMS. Furthermore, svm.js is used for a ready to go SVM implementation. As alternative, uncomment the code to use Naive Bayes classifier instead of SVM from the natural library.

machine-learning machine-learning-algorithms svm support-vector-machine support-vector-machines svm-classifier svm-training svm-learning svm-model svm-libraryPlease make sure you use the csv files inside the cvs_files/ directory and point to the right path inside your q code.

quantitative-trading neural-networks deep-learning random-forest support-vector-machines kdb kxSupport Vector Regression (SVR) analysis in Julia utilizing the libSVM library. SVR is a module of MADS (Model Analysis & Decision Support).

mads julia support-vector-machine regression model-assessment decision-support machine-learning high-performance-computing model-analysis model-reduction support-vector-regression support-vector-machines uncertainty-quantification sensitivity-analysis model-selection model-simulation model-predictions data-modeling data-analytics data-analysisIn 2018, The European Space Agency (ESA) organized a series of 6 lectures on Machine Learning at the European Space Operations Centre (ESOC). This repository contains the lectures resources: presentations, notebooks and links to the videos (presentation and hands-on).

machinelearning machine-learning linear-regression support-vector-machines decision-trees random-forest neural-network deep-learning clustering pca anomaly-detection text-mining tf-idf topic-modeling lectures lecture-slides lecture-material lecture-videosCreated a vehicle detection and tracking pipeline with OpenCV, histogram of oriented gradients (HOG), and support vector machines (SVM). Optimized and evaluated the model on video data from a automotive camera taken during highway driving. To run any notebook properly, copy the jupyter notebooks from the /ipynb folder to the root directory. This is so that each notebook sees relevant files, the most relevant files being the python classes.

udacity self-driving-car support-vector-machines vehicle-tracking hog-features
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