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...
人工知能:Artificial Intelligence
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
スパースモデリング Protected: Theory of Noisy L1-Norm Minimization as Machine Learning Based on Sparsity (1)
Theory of L1 norm minimization with noise as sparsity-based machine learning for digital transformation, artificial intelligence, and machine learning tasks Markov's inequality, Heffding's inequality, Berstein's inequality, chi-square distribution, hem probability, union Bound, Boolean inequality, L∞ norm, multidimensional Gaussian spectrum, norm compatibility, normal distribution, sparse vector, dual norm, Cauchy-Schwartz inequality, Helder inequality, regression coefficient vector, threshold, k-sparse, regularization parameter, inferior Gaussian noise
推論技術:inference Technology Protected: Explainable Artificial Intelligence (10) Model-independent Interpretation (Feature Interaction)
Interaction of features, one of the posterior interpretive models that can be explained and used for digital transformation (DX), artificial intelligence (AI), and machine learning (ML).
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
IOT技術:IOT Technology Protected: Leveraging Apache Spark for Distributed Data Processing – Developing and Executing Applications
Leveraging Apache Spark to enable distributed data processing for digital transformation, artificial intelligence, and machine learning tasks -Application development and execution (forced termination, yarn-client mode, yarn-cluster mode, YARN, and YARN) management commands, cluster, python, Clojure, Shell, AWS, Glue, sparkplug, spark-shell, spark-submit, Nodemanager, HDFS, Spark applications, Scala, sbt, plugin.sbt, build.sbt build.sbt, build, sbt-assembly plugin, JAR file)
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