線形代数:Linear Algebra

Protected: What triggers sparsity and for what kinds of problems is sparsity appropriate?

What triggers sparsity and for what kinds of problems is sparsity suitable for sparse learning as it is utilized in digital transformation, artificial intelligence, and machine learning tasks? About alternating direction multiplier method, sparse regularization, main problem, dual problem, dual extended Lagrangian method, DAL method, SPAMS, sparse modeling software, bioinformatics, image denoising, atomic norm, L1 norm, trace norm, number of nonzero elements

2023.01.09

アルゴリズム:Algorithmsスパースモデリングスパースモデリング微分積分:Calculus最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics線形代数:Linear Algebra

Overview of meta-heuristics and reference books

Overviews Meta-heuristics can be algorithms used to solve optimization problems. An optimization problem is on...

2023.01.08

アルゴリズム:Algorithmsグラフ理論幾何学:Geometry微分積分:Calculus最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics線形代数:Linear Algebra集合論:Set theory

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)

2023.01.04

アルゴリズム:Algorithms強化学習微分積分:Calculus最適化:Optimization機械学習:Machine Learning深層学習:Deep Learning確率・統計:Probability and Statistics線形代数:Linear Algebra

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

2022.12.29

Clojureアルゴリズム:Algorithms最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics線形代数:Linear Algebra

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

2022.12.28

アルゴリズム:Algorithms微分積分:Calculus最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics線形代数:Linear Algebra集合論:Set theory

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)

2022.12.25

アルゴリズム:Algorithmsグラフ理論スパースモデリング幾何学:Geometry微分積分:Calculus最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics線形代数:Linear Algebra

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

2022.12.24

アルゴリズム:Algorithmsグラフ理論幾何学:Geometry微分積分:Calculus最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics線形代数:Linear Algebra集合論:Set theory

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

2022.12.23

アルゴリズム:Algorithmsオンライン学習バンディッド問題強化学習微分積分:Calculus最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics線形代数:Linear Algebra

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.

2022.12.22

アルゴリズム:Algorithms微分積分:Calculus最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics線形代数:Linear Algebra集合論:Set theory

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)

2022.12.20

アルゴリズム:Algorithms微分積分:Calculus最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics線形代数:Linear Algebra集合論:Set theory