コンピューター Introduction to FPGAs for Software Engineers Machine Learning
Summary
An FPGA (Field Programmable Gate Array) is a programmable hardware device that can perform high-...
人工知能:Artificial Intelligence
コンピューター セマンテックウェブ技術:Semantic web Technology KI 2018: Advances in Artificial Intelligence Papers
KI2018
The German Conference on Artificial Intelligence (abbreviated KI) evolved from informal...
グラフ理論 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)
バンディッド問題 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).
微分積分:Calculus 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
アルゴリズム:Algorithms 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
アルゴリズム:Algorithms Protected: Supervised learning and regularization
Overview of supervised learning regression, discriminant and regularization ridge function, L1 regularization, bridge regularization, elastic net regularization, SCAD, group regularization, generalized concatenated regularization, trace norm regularization as the basis of machine learning optimization methods used for digital transformation, artificial intelligence and machine learning tasks
アルゴリズム:Algorithms Protected: Spatial statistics of Gaussian processes, with application to Bayesian optimization
Spatial statistics of Gaussian processes as an application of stochastic generative models used in digital transformation, artificial intelligence, and machine learning tasks, and tools ARD, Matern kernelsfor Bayesian optimization GPyOpt and GPFlow and GPyTorch
アルゴリズム:Algorithms Protected: Implementation of model-free reinforcement learning in python (1) epsilon-greedy method
Implementation in python of the epsilon-Greedy method, a model-free reinforcement learning method for use in digital transformation, artificial intelligence, and machine learning tasks, multi-armed bandit
Clojure Hierarchical Temporal Memory and Clojure
Deep learning with hierarchical temporal memory and sparse distributed representation with Clojure for digital transformation (DX), artificial intelligence (AI), and machine learning (ML) tasks