機械学習:Machine Learning

Protected: Overview of Discriminant Adaptive Losses in Statistical Mathematics Theory

Overview of Discriminant Conformal Losses in Statistical Mathematics Theory (Ramp Losses, Convex Margin Losses, Nonconvex Φ-Margin Losses, Discriminant Conformal, Robust Support Vector Machines, Discriminant Conformity Theorems, L2-Support Vector Machines, Squared Hinge Loss, Logistic Loss, Hinge Loss, Boosting, Exponential Losses, Discriminant Conformity Theorems for Convex Margin Losses, Bayes Rules, Prediction Φ-loss, Prediction Discriminant Error, Monotonic Nonincreasing Convex Function, Empirical Φ-loss, Empirical Discriminant Error)

2022.12.08

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: Online Stochastic Optimization and Stochastic Gradient Descent for Machine Learning

Stochastic optimization and stochastic gradient descent methods for machine learning for digital transformation DX, artificial intelligence AI and machine learning ML task utilization

2022.12.07

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: Optimality conditions and algorithm stopping conditions in machine learning

Optimality conditions and algorithm stopping conditions in machine learning used in digital transformation, artificial intelligence, and machine learning scaling, influence, machine epsilon, algorithm stopping conditions, iterative methods, convex optimal solutions, constrained optimization problems, global optimal solutions, local optimal solutions, convex functions, second order sufficient conditions, second order necessary conditions, first order necessary conditions

2022.12.06

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: Unsupervised Learning with Gaussian Processes (2) Extension of Gaussian Process Latent Variable Model

Extension of Gaussian process latent variable models as unsupervised learning by Gaussian processes, an application of stochastic generative models utilized in digital transformation, artificial intelligence, and machine learningtasks ,infinite warp mixture models, Gaussian process dynamics models, Poisson point processes, log Gaussian Cox processes, latent Gaussian processes, elliptic slice sampling

2022.12.05

アルゴリズム:Algorithmsグラフ理論ベイズ推定微分積分:Calculus推論技術:inference Technology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: Implementation of Model-Free Reinforcement Learning in python (3)Using experience for value assessment or strategy update: Value-based vs. policy-based

Value-based and policy-based implementations of model-free reinforcement learning in python for digital transformation, artificial intelligence, and machine learning tasks

2022.12.02

python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確率・統計:Probability and Statistics

Protected: Stochastic gradient descent implementation using Clojure and Hadoop

Stochastic gradient descent implementation using Clojure and Hadoop for digital transformation, artificial intelligence, and machine learning tasks (mini-batch, Mapper, Reducer, Parkour, Tesser, batch gradient descent, join-step Partitioning, uberjar, Java, batch gradient descent, stochastic gradient descent, Hadoop cluster, Hadoop distributed file system, HDFS)

2022.12.01

ClojureLarge-Scaleデータ微分積分:Calculus最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics線形代数:Linear Algebra

Implementation of Neural Networks and Error Back Propagation using Clojure

Implementation of neural nets and error back propagation using Clojure for digital transformation (DX), artificial intelligence (AI), and machine learning (ML) tasks

2022.11.29

アルゴリズム:Algorithms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: Information Geometry of Positive Definite Matrices (2) From Gaussian Graphical Models to Convex Optimization

Information geometry of positive definite matrices utilized in digital transformation, artificial intelligence, and machine learning tasks From Gaussian graphical models to convex optimization (chordal graphs, triangulation graphs, dual coordinates, Pythagorean theorem, information geometry, geodesics, sample variance-covariance matrix, maximum likelihood Estimation, divergence, knot space, Riemannian metric, multivariate Gaussian distribution, Kullback-Leibler information measure, dual connection, Euclidean geometry, narrowly convex functions, free energy)

2022.11.26

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 -Theoretical Limitations and the ε-Greedy Method

Theoretical limits and ε-greedy method, UCB method, riglet lower bounds for consistent measures, and KL divergence as measures for stochastic banded problems utilized in digital transformation , artificial intelligence , and machine learning tasks

2022.11.25

アルゴリズム:Algorithms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: Complexity of Hypothesis Sets in Statistical Mathematics Theory

Complexity of sets of hypotheses in statistical mathematical theory used in digital transformation, artificial intelligence, and machine learning tasks Rademacher complexity, VC dimension, large number factor, law of large uniform numbers, decision stocks, set of linear discriminators, set of linear functions, Cauchy-Schwartz inequality, Jensen inequality, Masar's complement, Talagrande's complement, empirical Rademacher complexity, Sauer's complement, Radon's theorem

2022.11.24

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