機械学習:Machine Learning

Protected: Stochastic Optimization and Online Optimization Overview

Stochastic and online optimization used in digital transformation, artificial intelligence, and machine learning tasks expected error, riglet, minimax optimal, strongly convex loss function, stochastic gradient descent, stochastic dual averaging method, AdaGrad, online stochastic optimization, batch stochastic optimization

2022.11.23

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

Protected: Unconstrained optimization for continuous optimization in machine learning

Unconstrained Optimization for Continuous Optimization in Machine Learning for Digital Transformation, Artificial Intelligence, and Machine Learning tasks machine epsilon, stopping conditions without scaling, stopping conditions with scaling, Taylor's theorem, stopping conditions for optimization algorithms, Hesse matrix

2022.11.22

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: Unsupervised Learning with Gaussian Processes (1)Overview and Algorithm of Gaussian Process Latent Variable Models

Overview and algorithms of unsupervised learning using Gaussian Process Latent Variable Models GPLVM, an application of probabilistic generative models used in digital transformation, artificial intelligence, and machine learning, Bayesian Gaussian Process Latent Variable Models ,Bayesian GPLVM

2022.11.21

アルゴリズム:Algorithmsベイズ推定微分積分:Calculus推論技術:inference Technology数理論理学:Mathematical logic最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics線形代数:Linear Algebra

Protected: Implementation of model-free reinforcement learning in python (2) Monte Carlo and TD methods

Python implementations of model-free reinforcement learning such as Monte Carlo and TD methods Q-Learning, Value-based methods, Monte Carlo methods, neural nets, Epsilon-Greedy methods, TD(lambda) methods, Muli-step Learning, Rainbow, A3C/A2C, DDPG, APE-X DDPG, Muli-step Learning) Epsilon-Greedy method, TD(λ) method, Muli-step Learning, Rainbow, A3C/A2C, DDPG, APE-X DQN

2022.11.17

アルゴリズム:Algorithmsマルチエージェントシステム強化学習微分積分:Calculus最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics線形代数:Linear Algebra集合論:Set theory

Protected: Clojure implementation of distributed computation processing (map-reduce) used in Hadoop

Clojure implementation of distributed computation processing (map-reduce) used in Hadoop for digital transformation, artificial intelligence, and machine learning tasks Tesser, Reducer function, fold, cost function, gradient descent method, feature extraction, feature-scales function, feature scaling, gradient descent learning rate, gradient descent update rule, iterative algorithm, multiple regression, correlation matrix, fuse, commutative, linear regression, co-reduction, and covariance) feature-scales function, feature scaling, gradient descent learning rate, gradient descent update rule, iterative algorithm, multiple regression, correlation matrix, fuse, commutativity, linear regression, covariance, Hadoop, pararrel fold

2022.11.16

ClojureLarge-Scaleデータ機械学習:Machine Learning

Introduction to FPGAs for Software Engineers Machine Learning

Summary An FPGA (Field Programmable Gate Array) is a programmable hardware device that can perform high-...

2022.11.13

コンピューターハードウェア機械学習:Machine Learning

Protected: Information Geometry of Positive Definite Matrices (1) Introduction of dual geometric structure

Introduction of dual geometric structures as information geometry for positive definite matrices utilized in digital transformation, artificial intelligence, and machine learning tasks (Riemannian metric, tangent vector space, semi-positive definite programming problem, self-equilibrium, Levi-Civita connection, Riemannian geometry, geodesics, Euclidean geometry, ∇-geodesics, tangent vector, tensor quantity, dual flatness, positive definite matrix set)

2022.11.12

グラフ理論スパースモデリングベイズ推定数理論理学最適化: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: Fundamentals of Stochastic Bandid Problems

Basics of stochastic bandid problems utilized in digital transformation, artificial intelligence, and machine learning tasks (large deviation principle and examples in Bernoulli distribution, Chernoff-Heffding inequality, Sanov's theorem, Heffding inequality, Kullback-Leibler divergence, probability mass function, hem probability, probability approximation by central limit theorem).

2022.11.11

バンディッド問題強化学習微分積分:Calculus機械学習:Machine Learning確率・統計:Probability and Statistics線形代数:Linear Algebra

Protected: Fundamentals of Convex Analysis as a Basic Matter for Sequential Optimization in Machine Learning

Basics of convex analysis as a fundamental matter of continuous optimization utilized in digital transformation, artificial intelligence, and machine learning tasks subgradient, subdifferential, conjugate function, closed truly convex function, conjugate function, strongly convex function, closed truly convex function, upper and lower bounds on function values, Hesse matrix, epigraph, Taylor's theorem, relative interior, Ahuynh envelope, continuity, convex envelope, convex function, convex set

2022.11.10

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: Basic Framework of Statistical Mathematics Theory

Basic framework of statistical mathematics theory used in digital transformation, artificial intelligence, and machine learning tasks regularization, approximation and estimation errors, Höfding's inequality, prediction discriminant error, statistical consistency, learning algorithms, performance evaluation, ROC curves, AUC, Bayes rules, Bayes error, prediction loss, empirical loss

2022.11.09

アルゴリズム:Algorithmsグラフ理論微分積分:Calculus最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics線形代数:Linear Algebra