machine learning

IA (Intelligence Augmentation) overviews and its application cases

Intelligence Augmentation used for digital transformation, artificial intelligence, and machine learning tasks and its application examples (application examples in the agriculture sector, transportation sector, energy sector, defense sector application examples, application examples in the financial industry, application examples in the manufacturing industry, application examples in the educational domain, application examples in the medical domain, perception, understanding, judgment, decision, learning, problem solving, creativity, capability, complementation, amplification)

2023.03.04

ICT技術:ICT Technology人工知能:Artificial Intelligence数学:Mathematics

Protected: Explainable Artificial Intelligence (14)Model Independent Interpretation (Scoped Rules (Anchors))

Model-independent interpretation with Anchor as explainable machine learning leveraged for digital transformation, artificial intelligence, and machine learningtasks Python, anchor, Alibi, Java, Anchors, BatchSAR, tabular data, Multi-Armed Bandit, KL-LUCB, reinforcement learning, graph search algorithms, LIME

2023.03.03

アルゴリズム:Algorithms説明できる機械学習

Protected: Applying Neural Networks to Reinforcement Learning Applying Deep Learning to Strategy:Advanced Actor Critic (A2C)

Application of Neural Networks to Reinforcement Learning for Digital Transformation, Artificial Intelligence, and Machine Learning tasks Implementation of Advanced Actor Critic (A2C) applying deep learning to strategies (Policy Gradient method, Q-learning, Gumbel Max Trix, A3C (Asynchronous Advantage Actor Critic))

2023.03.02

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: Implementation of recommendation algorithm using Clojure/Mahout

Implementation of recommendation algorithms using Clojure/Mahout for digital transformation, artificial intelligence, and machine learning tasks information retrieval statistics, precision, recall, DCG, IDCG, Ideal Discounted Cumulative Gain, Discounted Cumulative Gain, Discounted Cumulative Gain, fall-out, F-measure, harmonic mean, RMSE, k-nearest neighbor method, Pearson correlation, Spearman's rank correlation coefficient, Pearson correlation similarity, similarity measure Jaccard distance, Euclidean distance, cosine distance, pairwise differences, item-based, user-based

2023.03.01

Clojure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

Systems Thinking Approach and the SDGs

Systems thinking approaches and SDGs that are useful in examining the challenges of tasks such as machine learning, artificial intelligence, and digital transformation leverage points, system dynamics modeling, stock & flow, system archetypes, loop diagrams, CLD, time-series change pattern graphs, BOT

2023.02.28

組織活性化:revitalize an organization経済学課題解決:Problem solving

Overview of Container Technology and Docker for Cloud Native

Overview of container technologies that enable cloud-native utilized for digital transformation, artificial intelligence, and machine learning tasks and Docker container images, build/push functionality, cri-o, container execution, Kubernetes, the containerd, CRI-Containerd, low-level container runtime, high-level container runtime, kernel functionality, Open Container Initiative, OCI, Runtime Specification, Format Specification, Copy-on-Write, COW, cgroups, hierarchies, file systems, namespaces, virtual OS, Paas, Linux

2023.02.27

web技術:web technologyアーキテクチャコンピューターネットワーク技術プログラミング言語:Programming Language

The computing elements and semiconductor chips that make up a computer

Computational elements and semiconductor chips that make up computers utilized for digital transformation, artificial intelligence, and machine learning tasks (switching, MOSFETs, disjunction, conjunction, negation, silicon, statistical physics, dopants, periodic table, binary operations, Boolean algebra, bits, bytes)

2023.02.25

アーキテクチャコンピューター人工知能:Artificial Intelligence

Protected: Optimal arm identification and A/B testing in the bandit problem_1

Optimal arm identification and A/B testing in bandit problems for digital transformation, artificial intelligence, and machine learning tasks Heffding's inequality, optimal arm identification, sample complexity, sample complexity, riglet minimization, cumulative riglet minimization, cumulative reward maximization, ε-optimal arm identification, simple riglet minimization, ε-best arm identification, KL-UCB strategy, KL divergence) cumulative reward maximization, ε-optimal arm identification, simple liglet minimization, ε-best arm identification, KL-UCB strategy, KL divergence, A/B testing of the normal distribution, fixed confidence, fixed confidence

2023.02.24

アルゴリズム:Algorithmsグラフ理論バンディッド問題幾何学: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: Overview of nu-Support Vector Machines by Statistical Mathematics Theory

Overview of nu-support vector machines by statistical mathematics theory utilized in digital transformation, artificial intelligence, and machine learning tasks (kernel functions, boundedness, empirical margin discriminant error, models without bias terms, reproducing nuclear Hilbert spaces, prediction discriminant error, uniform bounds Statistical Consistency, C-Support Vector Machines, Correspondence, Statistical Model Degrees of Freedom, Dual Problem, Gradient Descent, Minimum Distance Problem, Discriminant Bounds, Geometric Interpretation, Binary Discriminant, Experience Margin Discriminant Error, Experience Discriminant Error, Regularization Parameter, Minimax Theorem, Gram Matrix, Lagrangian Function).

2023.02.23

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集合論:Set theory

Protected: Stochastic coordinate descent as a distributed process for batch stochastic optimization

Stochastic coordinate descent as a distributed process for batch stochastic optimization utilized in digital transformation, artificial intelligence, and machine learning tasks (COCOA, convergence rate, SDCA, γf-smooth, approximate solution of subproblems, stochastic coordinate descent, parallel stochastic coordinate descent, parallel computing process, Communication-Efficient Coordinate Ascent, dual coordinate descent)

2023.02.22

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