微分積分:Calculus Machine Learning Professional Series: Topic Models Post-Reading Notes Topic models using probability generation models to extract sentence topics to be used in digital transformation (DX) and artificial intelligence (AI) tasks. 2021.11.28 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
C言語 Protected: MCMC Method for Stochastic Integral Calculations: Multivariate Metropolis Algorithm MCMC Method for Stochastic Integral Computation for Digital Transformation and Artificial Intelligence Tasks: The Multivariate Metropolis Algorithm 2021.11.27 C言語機械学習:Machine Learning確率・統計:Probability and Statistics自然言語処理:Natural Language Processing
C言語 Protected: A concrete algorithm for Markov chain Monte Carlo: Metropolis method (2) application and efficiency An Overview of MCMC Efficiency Using Metropolis Method for Stochastic Integral Computation for Digital Trasformation and Artificial Intelligence Tasks 2021.11.26 C言語機械学習:Machine Learning確率・統計:Probability and Statistics自然言語処理:Natural Language Processing
C言語 Protected: A concrete algorithm for Markov chain Monte Carlo: Metropolis method (1)Overview Overview of the Metropolis method in MCMC methods used for probability integration and other aspects of machine learning for digital transformation and artificial intelligence tasks. 2021.11.25 C言語機械学習:Machine Learning確率・統計:Probability and Statistics自然言語処理:Natural Language Processing
C言語 Protected: General Theory of MCMC Methods: Applying Markov Chains to Monte Carlo Methods Application of Markov Chains to Monte Carlo methods for efficient computation of probability/combination and other integrals for digital transformation and artificial intelligence tasks. 2021.11.24 C言語機械学習:Machine Learning確率・統計:Probability and Statistics自然言語処理:Natural Language Processing
web技術:web technology Chatbots: The Future Transformed by the Evolution of AI and Robotics – Reading Memo Chatbot technology trends, business development and future prospects for use in digital transformation and artificial intelligence tasks 2021.11.23 web技術:web technologyチャットボット自然言語処理:Natural Language Processing
LISP Paradigms of Artificial Intelligence Programming: Case Studies in Common Lisp Reading Memo Explanation of basic ideas of various artificial intelligence techniques using LISP (GPS, ELIZA, SUDENT, symbol processing systems, natural language processing systems, Prolog, expert systems, etc.) 2021.11.23 LISPprolog推論技術:inference Technology数理論理学:Mathematical logic
C言語 Protected: On probability, expectation and Monte Carlo methods Explanation of the Monte Carlo method, which is the basis of the Markov Chain Monte Carlo (MCMC) method used in integral calculations for machine learning used in digital transformation and artificial intelligence tasks. 2021.11.22 C言語python機械学習:Machine Learning確率・統計:Probability and Statistics自然言語処理:Natural Language Processing
推論技術:inference Technology Making Logic Part 4 – Logic is Interesting from Here Non-Classical Logic Reading Notes Fundamentals of logic (non-classical logic, multi-valued logic, intuitionistic logic, and haphazard logic) in mathematical logic used for reasoning mechanisms in artificial intelligence tasks. 2021.11.21 推論技術:inference Technology数理論理学:Mathematical logic
推論技術:inference Technology Creating Logic, Part 3 – Another Look at Logic Reading Notes Foundations of logic (deductive reasoning, syntax and semantics) in mathematical logic used for reasoning mechanisms in artificial intelligence tasks. 2021.11.20 推論技術:inference Technology数理論理学:Mathematical logic