Clojure UML, workflow data visualization tool plantUML
UML for digital transformation , artificial intelligence , and machine learning tasks, workflow data visualization tools plantUML
Clojure アルゴリズム:Algorithms Protected: Implementation of model-free reinforcement learning in python (1) epsilon-greedy method
Implementation in python of the epsilon-Greedy method, a model-free reinforcement learning method for use in digital transformation, artificial intelligence, and machine learning tasks, multi-armed bandit
Clojure Hierarchical Temporal Memory and Clojure
Deep learning with hierarchical temporal memory and sparse distributed representation with Clojure for digital transformation (DX), artificial intelligence (AI), and machine learning (ML) tasks
Clojure Database construction for microservices using next generation DB Datomic
Building databases with Clojure for microservices using Datomic, the next-generation DB used for digital transformation, artificial intelligence, and machine learning tasks (AWS, five-tuple, immutable, immutable, Datalog, Transactor, Peer Server).
Clojure Network analysis using Clojure (1) Width-first/depth-first search, shortest path search, minimum spanning tree, subgraphs and connected components
Network analysis using Clojure/loop for digital transformation , artificial intelligence and machine learning tasks, width-first/depth-first search, shortest path search, minimum spanning tree, subgraph and connected components
Symbolic Logic Integration of logic and rules with probability/machine learning
Integration of logic and rules with machine learning (inductive logic programming, statistical relational learning, knowledge-based model building, Bayesian nets, probabilistic logic learning, hidden Markov models) used for digital transformation, artificial intelligence, and machine learning tasks.
アーキテクチャ Protected: Transition from monolithic services to microservices and service design
Overview of migration from monolithic services and design guidelines for microservices leveraged for digital transformation, artificial intelligence, and machine learning tasks (release cycle and deployment process, boundary context, productivity, components, service-oriented architecture, database isolation)
アルゴリズム:Algorithms Fundamentals of Continuous Optimization – Calculus and Linear Algebra
Fundamentals of Continuous Optimization - Calculus and Linear Algebra (Taylor's theorem, Hesse matrix, Landau's symbol, Lipschitz continuity, Lipschitz constant, implicit function theorem, Jacobi matrix, diagonal matrix, eigenvalues, nonnegative definite matrix, positive definite matrix, subspace, projection, 1-rank update, natural gradient method, quasi Newton method, Sherman-Morrison formula, norm, Euclidean norm, p-norm, Schwartz inequality, Helder inequality, function on matrix space)
アルゴリズム:Algorithms Protected: Calculation of marginal likelihood, posterior mean, posterior covariance, and predictive distribution using variational Bayesian methods
Methods for computing marginal likelihoods, posterior means, posterior covariances, and predictive distributions in variational Bayesian methods for digital transformation, artificial intelligence , and machine learning tasks James Stein estimator, maximum likelihood estimation, empirical Bayes estimator, Bayesian free energy, hyperparameters, automatic relevance determination, linear regression models, stochastic complexity, log marginal likelihood empirical Bayesian learning, multinomial distribution models, posterior means, linear regression models
アルゴリズム:Algorithms Protected: Structural learning of graphical models
On learning graph structures from data in Bayesian networks and Markov probability fields (Max-Min Hill Climbing (MMHC), Chow-Liu's algorithm, maximizing the score function, PC (Peter Spirtes and Clark Clymoir) Algorithm, GS (Grow-Shrink) algorithm, SGS (Spietes Glymour and Scheines) algorithm, sparse regularization, independence condition)