Overview of mini-batch learning and examples of algorithms and implementations
Overview of mini-batch learning.
Minibatch learning is one of the most widely used and efficient learning me...
オンライン学習
Board Games and AI “Why Alpha Go Could Beat Humans” Reading Notes
Introduction
AlphaGo, a computer Go program developed by Google DeepMind, became the first computer Go prog...
Overview of online forecasting technology and various applications and implementations
About Online Forecasting
Online Prediction (Online Prediction) is a method that uses models to make predictio...
Overview of online learning and various algorithms, application examples and specific implementations
Online Learning
Online Learning is a method of learning by sequentially updating a model under conditions whe...
Parallel and Distributed Processing in Machine Learning
Parallel and Distributed Processing in Machine Learning
The learning process of machine learning requires hi...
Protected: Exp3.P measures and lower bounds for the adversarial multi-armed bandit problem Theoretical overview
Theoretical overview of Exp3.P measures and lower bounds for adversarial multi-arm bandit problems utilized in digital transformation, artificial intelligence, and machine learning tasks cumulative reward, Poly INF measures, algorithms, Arbel-Ruffini theorem, pseudo-riglet upper bounds for Poly INF measures, closed-form expressions, continuous differentiable functions, Audibert, Bubeck, INF measures, pseudo-riglet upper bounds for INF measures, random choice algorithms, optimal order measures, highly probable riglet upper bounds) closed form, continuous differentiable functions, Audibert, Bubeck, INF measures, pseudo-riglet lower bounds, random choice algorithms, measures of optimal order, highly probable riglet upper bounds
Theory and algorithms of various reinforcement learning techniques and their implementation in python
Theory and algorithms of various reinforcement learning techniques used for digital transformation, artificial intelligence, and machine learning tasks and their implementation in python reinforcement learning,online learning,online prediction,deep learning,python,algorithm,theory,implementation
Protected: Measures for Stochastic Bandid Problems Stochastic Matching Method and Thompson Extraction
Stochastic bandit problem measures utilized in digital transformation, artificial intelligence, and machine learning tasks Stochastic matching methods and Thompson extraction worst-case riglet minimization, problem-dependent riglet minimization, worst-case riglet upper bounds, problem-dependent riglet, worst-case riglet, and MOSS measures, sample averages, correction terms, UCB liglet upper bounds, adversarial bandit problems, Thompson extraction, Bernoulli distribution, UCB measures, stochastic matching methods, stochastic bandit, Bayesian statistics, KL-UCCB measures, softmax measures, Chernoff-Heffding inequality
Protected: Overview of model-based approach to reinforcement learning and its implementation in python
Overview of reinforcement learning with model-based approaches used for digital transformation, artificial intelligence, and machine learning tasks and its implementation in python Bellman Equation, Value Iteration, Policy Iteration
stochastic optimization
Stochastic optimization methods for solving large-scale learning problems on large amounts of data used in digital transformation, artificial intelligence, and machine learning tasks supervised learning and regularization, basics of convex analysis, what is stochastic optimization, online stochastic optimization, batch stochastic optimization, stochastic optimization in distributed environments