アルゴリズム:Algorithms Overview of meta-heuristics and reference books
Overviews
Meta-heuristics can be algorithms used to solve optimization problems. An optimization problem is on...
アルゴリズム:Algorithms
アルゴリズム:Algorithms アルゴリズム:Algorithms Protected: Big Data and Bayesian Learning – The Importance of Small Data Learning
Big Data and Bayesian Learning for Digital Transformation (DX), Artificial Intelligence (AI), and Machine Learning (ML) Tasks - Importance of Small Data Learning
アルゴリズム:Algorithms Protected: Application of Neural Networks to Reinforcement Learning (2) Basic Framework Implementation
Implementation of a basic framework for reinforcement learning with neural networks utilized for digital transformation, artificial intelligence and machine learning tasks (TensorBoard, Image tab, graphical, real-time, progress check, wrapper for env. Observer, Trainer, Logger, Agent, Experience Replay, episode, action probability, policy, Epsilon-Greedy method, python)
Clojure Protected: Statistical analysis and correlation evaluation using Clojure/Incanter
Statistical analysis and correlation evaluation using Clojure for digital transformation, artificial intelligence, and machine learning tasks cumulative probability, confidence interval, standard deviation, population, 95% confidence interval, two-tailed test, z-transform, Fisher z-transform, cumulative distribution function, t-distribution, one-tailed test, and degrees of freedom, sampling error, null hypothesis, alternative hypothesis, hypothesis test, standard score, Pearson's product ratio correlation coefficient, covariance, jittering, lognormal distribution, Beki power, Gibrat's law, histogram
アルゴリズム:Algorithms Protected: Online-type stochastic optimization for machine learning with AdaGrad and minimax optimization
Online stochastic optimization and AdaGrad for machine learning utilized in digital transformation, artificial intelligence, and machine learning tasks, minimax optimization sparsity patterns, training errors, batch stochastic optimization, online stochastic optimization, batch gradient method, minimax optimality, generalization error, Lipschitz continuity, strong convexity, minimax optimal error, minimax error evaluation, first-order stochastic oracle, stochastic dual averaging method, stochastic gradient descent method, regular terms, Nemirovsky, Yudin, convex optimization method, expected error bound, riglets, semidefinite matrix, mirror image descent method, soft threshold functions
アルゴリズム:Algorithms Geometric approach to data
Geometric approaches to data utilized in digital transformation, artificial intelligence, and machine learning tasks (physics, quantum information, online prediction, Bregman divergence, Fisher information matrix, Bethe free energy function, the Gaussian graphical models, semi-positive definite programming problems, positive definite symmetric matrices, probability distributions, dual problems, topological, soft geometry, topology, quantum information geometry, Wasserstein geometry, Lupiner geometry, statistical geometry)
アルゴリズム:Algorithms Topological handling of data using topological data analysis
Topological handling of data using topological data analysis utilized for digital transformation, artificial intelligence, and machine learning tasks application to character recognition, application to clustering, R, TDA, barcode plots, persistent plots , python, scikit-tda, Death - Birth, analysis of noisy data, alpha complex, vitris-lips complex, check complex, topological data analysis, protein analysis, sensor data analysis, natural language processing, soft geometry, hard geometry, information geometry, Euclidean Spaces
アルゴリズム:Algorithms Protected: Measures for Stochastic Bandid Problems Stochastic Matching Method and Thompson Extraction
Stochastic bandit problem measures utilized in digital transformation, artificial intelligence, and machine learning tasks Stochastic matching methods and Thompson extraction worst-case riglet minimization, problem-dependent riglet minimization, worst-case riglet upper bounds, problem-dependent riglet, worst-case riglet, and MOSS measures, sample averages, correction terms, UCB liglet upper bounds, adversarial bandit problems, Thompson extraction, Bernoulli distribution, UCB measures, stochastic matching methods, stochastic bandit, Bayesian statistics, KL-UCCB measures, softmax measures, Chernoff-Heffding inequality
アルゴリズム:Algorithms Protected: Kernel functions as the basis of kernel methods in statistical mathematics theory.
Kernel functions (Gaussian kernels, polynomial kernels, linear kernels, kernel functions, regression functions, linear models, regression problems, discriminant problems) as the basis for kernel methods in statistical mathematics theory used in digital transformation, artificial intelligence and machine learning tasks.
アルゴリズム:Algorithms Protected: Basics of gradient method (linear search method, coordinate descent method, steepest descent method and error back propagation method)
Fundamentals of gradient methods utilized in digital transformation, artificial intelligence, and machine learning tasks (linear search, coordinate descent, steepest descent and error back propagation, stochastic optimization, multilayer perceptron, adaboost, boosting, Wolf condition, Zotendijk condition, Armijo condition, backtracking methods, Goldstein condition, strong Wolf condition)