アルゴリズム:Algorithms Example implementation for general time series analysis using R or Python
Machine Learning Artificial Intelligence Digital Transformation ICT Sensor Data & IOT ICT Infrastructure Stream Data...
数学:Mathematics
アルゴリズム:Algorithms アルゴリズム:Algorithms Overview of LightGBM and its implementation in various languages
Machine learning Mathematics Artificial Intelligence Digital Transformation Algorithms and Data Structures Image Recogni...
python Overview of machine learning and data analysis in Python and introduction to typical libraries
Commentary on libraries and reference books on data analysis using Pyhon, which is used for digital transformation and artificial intelligence
アルゴリズム:Algorithms Time series analysis using Prophet
Machine Learning Artificial Intelligence Digital Transformation ICT Sensor Data & IOT ICT Infrastructure Stream Data...
アルゴリズム:Algorithms Protected: Hidden Markov model building and fully decomposed variational inference in Bayesian inference
Hidden Markov model building and fully decomposed variational inference (approximate posterior distribution, categorical distribution, Dirichlet distribution, expectation calculation, transition probability matrix, Poisson mixture model, variational inference) in Bayesian inference for digital transformation, artificial intelligence, machine learning tasks.
アルゴリズム:Algorithms Protected: Research Trends in Deep Reinforcement Learning: Meta-Learning and Transfer Learning, Intrinsic Motivation and Curriculum Learning
Research trends in deep reinforcement learning for digital transformation, artificial intelligence, and machine learning tasks: meta-learning and transfer learning, intrinsic motivation and curriculum learning automatic curriculum generation, automatic task decomposition, task difficulty adjustment, intrinsic reward, robot domain transformation, robot domain transformation, simulator to simulator transfer learning, BERT, Metric/Representation Base, Memory/Knowledge Base, active learning, meta-learning, and robot domain transformation) Robot domain transformation, transfer learning from simulators, BERT, Model-Agnostic Meta-Learning, Active Learning, Metric/Representation Base, Memory/Knowledge Base, Weigh Base, and Learning to Optimize
アルゴリズム:Algorithms Protected: Optimal arm bandit and Bayesian optimal when the player’s candidate actions are huge or continuous (2)
Bayesian optimization for digital transformation, artificial intelligence, machine learning tasks and bandit when player behavior is massive/continuous Markov chain Monte Carlo, Monte Carlo integration, turn kernels, scale parameters, Gaussian kernels, covariance function parameter estimation, Simultaneous Optimistic Optimazation policy, SOO strategy, algorithms, GP-UCB policy, Thompson's law, expected value improvement strategy, GP-UCB policy
アルゴリズム:Algorithms Protected: Sparse Machine Learning with Overlapping Sparse Regularization
Sparse machine learning with overlapping sparse regularization for digital transformation, artificial intelligence, and machine learning tasks main problem, dual problem, relative dual gap, dual norm, Moreau's theorem, extended Lagrangian, alternating multiplier method, stopping conditions, groups with overlapping L1 norm, extended Lagrangian, prox operator, Lagrangian multiplier vector, linear constraints, alternating direction multiplier method, constrained minimization problem, multiple linear ranks of tensors, convex relaxation, overlapping trace norm, substitution matrix, regularization method, auxiliary variables, elastic net regularization, penalty terms, Tucker decomposition Higher-order singular value decomposition, factor matrix decomposition, singular value decomposition, wavelet transform, total variation, noise division, compressed sensing, anisotropic total variation, tensor decomposition, elastic net
機械学習:Machine Learning Linear Algebra Overview and Library and Reference Books
Machine Learning Artificial Intelligence Digital Transformation Natural Language Processing Deep Learning Information Ge...
アルゴリズム:Algorithms Protected: Optimization for the main problem in machine learning
Optimization for main problems in machine learning used in digital transformation, artificial intelligence, and machine learning tasks (barrier function method, penalty function method, globally optimal solution, eigenvalues of Hesse matrix, feasible region, unconstrained optimization problem, linear search, Lagrange multipliers for optimality conditions, integration points, effective constraint method)