42 skills found · Page 1 of 2
Dicklesworthstone / Fast Vector SimilarityHigh-performance vector similarity library in Rust with Python bindings: Spearman, Kendall, distance correlation, Jensen-Shannon, Hoeffding's D, and bootstrapped confidence intervals
zhenxingjian / Partial Distance CorrelationThis is the official GitHub for paper: On the Versatile Uses of Partial Distance Correlation in Deep Learning, in ECCV 2022
JuliaDynamics / Associations.jlAlgorithms for quantifying associations, independence testing and causal inference from data.
vnmabus / DcorDistance correlation and related E-statistics in Python
xiangwang1223 / Disentangled Graph Collaborative FilteringDisentagnled Graph Collaborative Filtering, SIGIR2020
CIRCL / Douglas QuaidOpen source software for image correlation, distance and analysis
mariarizzo / Energyenergy package for R
CIRCL / Carl HauserOpen Source testing framework for image correlation, distance and analysis
pierrebaudot / Infotopopycomputes most of information functions (joint entropy, conditional, mutual information, total correlation information distance) and deep information networks
jwcarr / MantelPython implementation of the Mantel test, a significance test of the correlation between two distance matrices
ip681 / Face Recognition With Correlation Coefficients And Euclidean DistanceNo description available
vios-s / CSDisentanglement Metrics LibraryThis repository constists of the implementations of the Distance Correlation (DC) and Information Over Bias (IOB) metrics proposed in [link]. The two metrics can be used to assess the level of disentanglement between spatial content and vector style representations. Both metrics are ready to use with PyTorch and TensorFlow implementations.
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!
majianthu / Aps2020Code for the paper 'Variable Selection with Copula Entropy' published on Chinese Journal of Applied Probability and Statistics
iosonofabio / Lshknnk nearest neighbor (KNN) graphs via Pearson correlation distance and local sensitive hashing (LSH).
felix-laumann / SDG NetworksFinding nonlinear relationships between the Sustainable Development Goals and climate change with partial distance correlations
DevD1092 / Spr Ham SpearRepository for simulation and testing codes of star identification algorithm based on hamming distance and spearman-correlation
Mamba413 / CdcsisConditional Distance Correlation based Statistical Method
JuliaNeuroscience / SpikeSynchrony.jlJulia implementations for measuring distances, synchrony and correlation between spike trains
singhman / MovieRecommendationEngineAn user based and item based movie rating prediction recommender system based on data provided by MovieLens using memory-based Collaborative filtering technique by utilizing Pearson correlation, Euclidean distances, Cosine distances, and K-nearest neighbors algorithms
