C/C++ Examples of iteration and branching implementations in various languages
summary
Among programming languages, the basic functionality is one element of the three functions of stru...
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Deux Ex Machina
C/C++ 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
Prophet Overview
Prophet is a time-series forecasting tool developed by Facebook that will be able to...
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.
テクノロジー:Technology Technology Discussion – A look at some of the latest technologies
Technology Discussion - A look at some of the latest technologies
Introduction
This section discusses v...
旅 On the road, Hongo neighborhood
Summary
Travel is an act for human beings to visit new places and experience different cultures and histories. T...
アート:Art Read the classics again
Reread the classics
The Significance of Rereading the Classics
The term "classic" refers to a work in a field of...
哲学:philosophy Protected: Special Lecture, “The Significance of Dialogue,” from “Socrates in Defense.”
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アルゴリズム:Algorithms Protected: Explainable Machine Learning (19)prototype and criticism
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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