20 skills found
dgleich / Matlab BglA graph library for Matlab based on the boost graph library
kwonoh / GlamGLAM: Graph Layout Aesthetic Metrics
skramm / UdgcdUnDirected Graph Cycle Detection: C++ wrapper over Boost Graph Library (BGL)
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!
kulpreet / Lightning Network Graph AnalysisAnalyse the lightning network graph
erwinvaneijk / Bgl PythonBoost Graph Library - Python interface. This is the repository with the imported repository from Douglas Gregor
leonardoarcari / ArlibC++ Alternative Routing Library for Boost.Graph. A configurable, efficient, plug-n-play solution for alternative route planning and k-shortest paths problems.
daviddoria / BGLExamplesExamples of how to use features of the Boost Graph Library. This repository is a staging area while the examples are prepared to be moved one at a time to the Boost repository. Examples that have been moved already can be found here: https://svn.boost.org/svn/boost/trunk/libs/graph/doc/table_of_contents.html
rafaelglikis / Dynamic ConnectivityThere is given an undirected graph G = (V, E) from which edges are deleted one at a time. Questions like "Are the vertices u and v in the same connected component?" have to be answered in constant time.
iszczesniak / YenThe implementation of the Yen K-shortest path algorithm with the Boost Graph Library
miguelpais / Simple Social Network AnalysisSocial Network Analysis on top of Boost C++ Graph Library
temp3rr0r / InfectiousDiseaseModellingParallel computing: Infectious Disease Modeling with OpenMP. Agent based modelling on Undirected Acyclic graphs.
LiangliangNan / Tutorial BoostGraphLibrarySimple examples showing how to use the Boost Graph Library
aaw / Boost Planar Graph DualCode for finding the dual of a planar graph with the boost graph library
duffee / Boost GraphRelease history of Boost-Graph
wpm / Boost Implicit Graph ExampleExample of a simple implicit graph created with the Boost Graph Library.
mikael-s-persson / Boost Graph Ext MpThis is a collection of Boost Graph Library extensions soon to be proposed.
phisco / Advanced Algorithms ProjectStrongly connected component algorithms implementations using BGL (boost graph library)
chut89 / ClonedGraphvizA simple reimplementation of Graphviz dotgen algorithms using Boost graph library
Mik1810 / Nxppnxpp is a modular C++20 graph library built on top of Boost Graph Library, offering a practical NetworkX-inspired API for graph construction, traversal, shortest paths, components, flows, and generators, with a generated single-header release asset also available.
