Deus Ex Machina

Strolling along the street in Akasaka

Akasaka Stroll from Ryotaro Shiba's Kaido Yuku (Hie Shrine, Nogizaka46, Nogizaka, Nogi Shrine, Korekiyo Takahashi Memorial Park, Nijuoroku Incident, Korekiyo Takahashi, Toyokawa Inari, Omotesando, Kukai, Soto sect, Myogonji, Dogen, Esoteric Buddhism, Dakini Koten, fox, Ukanomikoto, Inari Shrine, Josui Organization, Hikawa Shrine, Yoshimune Tokugawa, Okura Shukokan, Kingo Tatsuno, Tokyo Station, Sakuradamura, Tameike Sanno, Machitobi, Teishigake, Gagan, Hotel New Okura, American Embassy

2023.01.15

旅歴史紀行読書

Living like water – the fundamental path of Lao Tzu’s philosophy

Who is Lao Tzu? The ancient Chinese book of thought "Laozi" was written by a man named Laozi, but his career is ...

2023.01.14

中国古典:classics哲学:philosophy

Protected: The Vimalakaya Sutra: A Major Shift in Buddhist Thought

Vima Sutra- One of the great transformations of Buddhist thought (Vima, Dharma, illness, Dharma products, Six Perfections, Four Immeasurable Minds, Compassion, Sadness, Joy, Renunciation, Four Celestial Laws, Fuse, Aikoku, Ri Gyo, Simultaneous, Buddhist national articles, Fuse, Mochikai, Ninshin, Seishin, Zensho, Wisdom, Birth and Heaven Thought, Dharma Body, Rewarding Body, Oyumi, Emptiness, Otherness, Sociality, Compassion, Sangyo Giso, and Shotoku Taishi, Biyarai, Lotus Sutra, Shoman Sutra, Arhat, Bodhisattva Path, Prajnaparamita Sutra, Lotus Sutra, Kegon Sutra, Sutra of Immeasurable Life, Mahayana Buddhism, Deconstruction Device, Arya Sutra, Middle Agony Sutra, Sakyamuni, Developmental Buddhism, Murakami Senju, ordained, ordained practitioners)

2023.01.14

仏教:Buddhism哲学:philosophy

Protected: Regret Analysis for Stochastic Banded Problems

Regret analysis for stochastic banded problems utilized in digital transformation, artificial intelligence, and machine learning tasks (sum of equal sequences, gamma function, Thompson extraction, beta distribution, hem probability, Mills ratio, partial integration, posterior sample, conjugate prior distribution, Bernoulli distribution, cumulative distribution function, expected value, DMED measure, UCB measure, Chernoff-Hefding inequality, likelihood, upper bound, lower bound, UCB score, arms)

2023.01.13

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線形代数:Linear Algebra

Protected: Regenerate nuclear Hilbert spaces as a basis for kernel methods in statistical mathematics theory.

Regenerate kernel Hilbert spaces as a basis for kernel methods in statistical mathematics theory used in digital transformation, artificial intelligence, and machine learning tasks orthonormal basis, Hilbert spaces, Gaussian kernels, continuous functions, kernel functions, complete spaces, inner product spaces, equivalence classes, equivalence relations, Cauchy sequences, linear spaces, norms, complete inner products

2023.01.12

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: Batch Stochastic Optimization – Stochastic Dual Coordinate Descent

Stochastic dual coordinate descent algorithms as batch-type stochastic optimization utilized in digital transformation, artificial intelligence, and machine learning tasks Nesterov's measurable method, SDCA, mini-batch, computation time, batch proximity gradient method, optimal solution, operator norm, maximum eigenvalue , Fenchel's dual theorem, principal problem, dual problem, proximity mapping, smoothing hinge loss, on-line type stochastic optimization, elastic net regularization, ridge regularization, logistic loss, block coordinate descent method, batch type stochastic optimization

2023.01.11

アルゴリズム:Algorithms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

Protected: Newtonian and Modified Newtonian Methods as Sequential Optimization in Machine Learning

Newton and modified Newton methods (Cholesky decomposition, positive definite matrix, Hesse matrix, Newtonian direction, search direction, Taylor expansion) as continuous machine learning optimization for digital transformation, artificial intelligence and machine learning tasks

2023.01.10

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: 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: 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

2023.01.07

アルゴリズム:Algorithmsグラフ理論ベイズ推定幾何学:Geometry微分積分:Calculus最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics