77 skills found · Page 1 of 3
erikbern / EigenstuffUse the first eigenvector (stationary distribution) of Google searches for "move from X to Y" to say something about future popularity
thomeou / SALSAThis is the public repository for eigenvector-based SALSA features for polyphonic sound event localization and detection.
isce-framework / FringeFine Resolution InSAR With Generalized Eigenvectors (FRInGE)
ritchieng / Eigenvectors From EigenvaluesPyTorch implementation comparison of old and new method of determining eigenvectors from eigenvalues.
AdityaRoy-1996 / Phonopy VESTAExport Eigenvectors from Phonopy format to VESTA
juanitacabral / LEiDALeading Eigenvector Dynamics Analysis
felipeZ / Eigenvaluessymmetric matrices algorithms to compute eigenvalue/eigenvector pairs
clbustos / ExtendmatrixCosmin Bonchis's enhancements to the Ruby "Vector" and "Matrix" module and includes: LU and QR (Householder, Givens, Gram Schmidt, Hessenberg) decompositions, bidiagonalization, eigenvalue and eigenvector calculations. Work on Ruby 1.8.7, 1.9.1 and 1.9.2 (SVN version)
aishack / Dominant ColorsUse eigenvectors to find dominant colors in an image.
MIT-SPARK / Fast ShapeAndPoseShape and pose estimation via eigenproblem with eigenvector nonlinearities.
dgleich / GenericArpack.jlA pure Julia translation of the Arpack library for eigenvalues and eigenvectors but for any numeric types. (Symmetric only right now)
tobydriscoll / EigenShow.jlInteractive demonstrator of eigenvectors and singular vectors
PeterDenton / Eigenvector Eigenvalue IdentityNo description available
delton137 / PhononSEDCalculates Projected Phonon Spectral Energy Density (SED) from molecular dynamics atomic velocity data and phonon eigenvectors
reddyprasade / Machine Learning Interview PreparationPrepare to Technical Skills Here are the essential skills that a Machine Learning Engineer needs, as mentioned Read me files. Within each group are topics that you should be familiar with. Study Tip: Copy and paste this list into a document and save to your computer for easy referral. Computer Science Fundamentals and Programming Topics Data structures: Lists, stacks, queues, strings, hash maps, vectors, matrices, classes & objects, trees, graphs, etc. Algorithms: Recursion, searching, sorting, optimization, dynamic programming, etc. Computability and complexity: P vs. NP, NP-complete problems, big-O notation, approximate algorithms, etc. Computer architecture: Memory, cache, bandwidth, threads & processes, deadlocks, etc. Probability and Statistics Topics Basic probability: Conditional probability, Bayes rule, likelihood, independence, etc. Probabilistic models: Bayes Nets, Markov Decision Processes, Hidden Markov Models, etc. Statistical measures: Mean, median, mode, variance, population parameters vs. sample statistics etc. Proximity and error metrics: Cosine similarity, mean-squared error, Manhattan and Euclidean distance, log-loss, etc. Distributions and random sampling: Uniform, normal, binomial, Poisson, etc. Analysis methods: ANOVA, hypothesis testing, factor analysis, etc. Data Modeling and Evaluation Topics Data preprocessing: Munging/wrangling, transforming, aggregating, etc. Pattern recognition: Correlations, clusters, trends, outliers & anomalies, etc. Dimensionality reduction: Eigenvectors, Principal Component Analysis, etc. Prediction: Classification, regression, sequence prediction, etc.; suitable error/accuracy metrics. Evaluation: Training-testing split, sequential vs. randomized cross-validation, etc. Applying Machine Learning Algorithms and Libraries Topics Models: Parametric vs. nonparametric, decision tree, nearest neighbor, neural net, support vector machine, ensemble of multiple models, etc. Learning procedure: Linear regression, gradient descent, genetic algorithms, bagging, boosting, and other model-specific methods; regularization, hyperparameter tuning, etc. Tradeoffs and gotchas: Relative advantages and disadvantages, bias and variance, overfitting and underfitting, vanishing/exploding gradients, missing data, data leakage, etc. Software Engineering and System Design Topics Software interface: Library calls, REST APIs, data collection endpoints, database queries, etc. User interface: Capturing user inputs & application events, displaying results & visualization, etc. Scalability: Map-reduce, distributed processing, etc. Deployment: Cloud hosting, containers & instances, microservices, etc. Move on to the final lesson of this course to find lots of sample practice questions for each topic!
SishaarRao / PageRankA demonstration of the PageRank algorithm, using Eigenvectors to assign significance to HTML pages
adrelino / Pointcloud NormalsComputation of coherently aligned pointcloud normals using nanoflann for neighbour search and Eigen for eigenvector computation.
f-dangel / Vivit[TMLR 2022] Curvature access through the generalized Gauss-Newton's low-rank structure: Eigenvalues, eigenvectors, directional derivatives & Newton steps
HLTCHKUST / Eigenvector AnalysisCode for "Interpreting Word Embeddings with Eigenvector Analysis" https://openreview.net/forum?id=rJfJiR5ooX.
PSYMARKER / Leida PythonLeading Eigenvector Dynamics Analysis (LEiDA) Python Toolbox
