27 skills found
gu0y1 / Picture2pixelA Python library for converting images into FPGA-displayable pixel art.
je-suis-tm / Machine LearningPython machine learning applications in image processing, recommender system, matrix completion, netflix problem and algorithm implementations including Co-clustering, Funk SVD, SVD++, Non-negative Matrix Factorization, Koren Neighborhood Model, Koren Integrated Model, Dawid-Skene, Platt-Burges, Expectation Maximization, Factor Analysis, ISTA, FISTA, ADMM, Gaussian Mixture Model, OPTICS, DBSCAN, Random Forest, Decision Tree, Support Vector Machine, Independent Component Analysis, Latent Semantic Indexing, Principal Component Analysis, Singular Value Decomposition, K Nearest Neighbors, K Means, Naïve Bayes Mixture Model, Gaussian Discriminant Analysis, Newton Method, Coordinate Descent, Gradient Descent, Elastic Net Regression, Ridge Regression, Lasso Regression, Least Squares, Logistic Regression, Linear Regression
KingJamesSong / DifferentiableSVDA collection of differentiable SVD methods and ICCV21 "Why Approximate Matrix Square Root Outperforms Accurate SVD in Global Covariance Pooling?"
mayukh18 / Recoa simple yet versatile recommendation systems library in python
aedin / PCAworkshopAn introduction to matrix factorization and PCA and SVD.
JieniChen / Recommender SystemIn this code we implement and compared Collaborative Filtering algorithm, prediction algorithms such as neighborhood methods, matrix factorization-based ( SVD, PMF, SVD++, NMF), and many others.
SENATOROVAI / Singular Value Decomposition Svd Solver CourseSingular Value Decomposition (SVD) is a fundamental linear algebra technique that factorizes any into the product of three matrices: are orthogonal matrices containing left and right singular vectors, while sigma is a diagonal matrix of non-negative singular values. It is essential for data reduction, noise removal, and matrix approximation.Solver
soypat / Laplinear algebra package. like gonum/mat, but small. lets say gonum-lite
daniel-aime / Recommender System By Daniel AimeProjet d'étude système de recommendation en utilisant filtrage collaboratif
satyamt13 / Project Amazon Reviews NLP Recommender SystemMining , pre-processing and embedding over 1 million Amazon Movie & T.V. reviews to build a multi class Naive Bayes model and later a CNN-LSTM model (that uses the Naive Bayes model as a baseline) to predict rating from text. Interpreting the original classifier using local surrogate models using the LIME library. Using LDA topic modeling to build a theme based recommender from the reviews and using a model based collaborative filtering system using SVD matrix factorization to build a second recommender system.
gnsaddy / Book Recommendation System WebappBook Recommendation System Web App
zeryabmoussaoui / SVD Golub KahanSingular Value computation using Golub-Kahan method
singhsidhukuldeep / Recommendation SystemComparing different recommendation systems algorithms like SVD, SVDpp (Matrix Factorization), KNN Baseline, KNN Basic, KNN Means, KNN ZScore), Baseline, Co Clustering
khyatimahendru / EigenfacesWithSVDFacial Recognition on 'Labelled Faces in the Wild Dataset' using the concept of Eigenfaces. I have used Singular Value Decomposition to obtain the eigenfaces used.
pandeyankit83 / Deep Learning Recommender SystemYou can train a neural network with user ratings or purchases, and use it to make recommendations; deep learning can be very good at recognizing patterns in a way similar to how our brain may do it. It's good at things like image recognition and predicting sequences of events.Neural networks are fundamentally matrix operations and there are already well-established matrix factorization techniques for recommender systems that fundamentally do something similar. In SVD for example, we find matrices that we multiply together using weights that are learned from stochastic gradient descent, it's almost the same thing, just thought of in a different way. So yeah, you could think of recommender systems as looking for patterns, just very complex ones based on the behavior of other people. So a matrix factorization can be modeled as a neural network. I think the main reason to experiment with applying neural networks to recommender systems is that it lets us take advantage of all the rapid advances in the fields of AI and deep learning. Amazon, for example, has open-sourced a system called DSSTNE, that's D-S-S-T-N-E, which allows you to run huge neural networks that deal with sparse data, on a cluster, efficiently. They claim to be using this internally for their own recommender systems. There are also ways to use TensorFlow in a cluster, and take advantage of a whole fleet of GPUs. And there's always research on new topologies for neural networks that can lead to fresh insights on how to make better recommendations using them. In some cases, approaches using neural networks have been shown to outperform SVD already, even if it's by a rather small margin. So, let's dive into some ways you can apply neural networks to the problem of making recommendations.
oekosheri / Recommender MovieA movie recommender served with a Flask-restful app
Ayoub-etoullali / SVD Singular Value DecompositionThis project demonstrates the application of Singular Value Decomposition (SVD) for image compression using Python and NumPy.
d-elicio / Music Recommender System From ScratchDesign and implementation from scratch of different models for a musical recommendation system
MatheusAraujoSouza / MS512 Numerical Analysis I ProjectsThis course aimed at teaching methods such as Choleski factorization, orthogonal, linear least squares, SVD and Iterative methods for solving linear and non-linear systems.
alexandercameronh / TensorflowFM RecommenderRecommender system project - SVD system and Tensor Flow Matrix Factorization Engine system
