深層学習:Deep Learning

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

2022.11.02

Clojurepythonアルゴリズム:Algorithmsグラフ理論スパースモデリングスパースモデリング微分積分:Calculus時系列データ解析最適化:Optimization機械学習:Machine Learning深層学習:Deep Learning確率・統計:Probability and Statistics線形代数:Linear Algebra

Integration of logic and rules with probability/machine learning

Integration of logic and rules with machine learning (inductive logic programming, statistical relational learning, knowledge-based model building, Bayesian nets, probabilistic logic learning, hidden Markov models) used for digital transformation, artificial intelligence, and machine learning tasks.

2022.10.30

Symbolic Logicアルゴリズム:Algorithmsエキスパートシステム:expertsystemグラフ理論セマンテックウェブ技術:Semantic web Technologyベイズ推定微分積分:Calculus推論技術:inference Technology数理論理学:Mathematical logic最適化:Optimization機械学習:Machine Learning深層学習:Deep Learning確率・統計:Probability and Statistics関係データ学習

Protected: Machine Translation Today and Tomorrow – Different Machine Learning Approaches for Natural Language

Present and future of machine translation for digital transformation, artificial intelligence, and machine learning tasks - Different machine learning approaches for natural language machine translation based on attentional neural nets, machine translation based on encoding and decoding, recurrent neural nets, machine translation based on neural nets and neural models, neural net-based translation, tree-to-strong translation, pre-ordering, parse trees and parsing, word mapping, phrase-based translation

2022.10.15

アルゴリズム:Algorithmsグラフ理論ベイズ推定最適化:Optimization機械学習:Machine Learning深層学習:Deep Learning確率・統計:Probability and Statistics線形代数:Linear Algebra自然言語処理:Natural Language Processing

Protected: Equivalence of neural networks (deep learning) and Gaussian processes

On the equivalence of Gaussian processes and neural networks, an applied model of stochastic generative models used in digital transformation, artificial intelligence, and machine learning tasks from Neal's paper

2022.10.01

アルゴリズム:Algorithmsベイズ推定微分積分:Calculus最適化:Optimization機械学習:Machine Learning深層学習:Deep Learning確率・統計:Probability and Statistics線形代数:Linear Algebra

Protected: Overviews of reinforcement learning and implementation of a simple MDP model

Overview of reinforcement learning used for digital transformation (DX), artificial intelligence (AI), and machine learning (ML) tasks and implementation of a simple MDP model in python

2022.09.30

アルゴリズム:Algorithms強化学習微分積分:Calculus最適化:Optimization機械学習:Machine Learning深層学習:Deep Learning確率・統計:Probability and Statistics線形代数:Linear Algebra

Protected: Overview and history of the banded problem and its relationship to reinforcement learning/online learning

Overview and history of bandit problems utilized in digital transformation, artificial intelligence, and machine learning tasks and their relationship to reinforcement learning online learning

2022.09.16

アルゴリズム:Algorithmsバンディッド問題強化学習機械学習:Machine Learning深層学習:Deep Learning

Protected: Meta-analysis in Medical Research Methods of Evidence Integration in Scientific Evidence-Based Medicine

Evidence integration in meta-analysis in science-based medicine as statistical data processing in digital transformation, artificial intelligence, and machine learning tasks method of moments, maximum likelihood, large sample theory, DerSimonian an Laird estimation, publication bias, network meta-analysis

2022.09.04

アルゴリズム:Algorithmsシミュレーションスパースモデリングベイズ推定最適化:Optimization検索技術:Search Technology深層学習:Deep Learning確率・統計:Probability and Statistics関係データ学習

Optimization for the First Time Reading Notes

Optimization for the First Time Reading Notes From Optimization for Beginners This book is explained in d...

2022.08.06

グラフ理論ベイズ推定微分積分:Calculus最適化:Optimization機械学習:Machine Learning深層学習:Deep Learning確率・統計:Probability and Statistics線形代数:Linear Algebra集合論:Set theory

Protected: Reconstructing the shape of celestial objects from time series data – Temporal Astronomy

Reconstruct the shape of celestial objects (V455 Andromeda, accretion disk structure, white dwarf) from time series data using Bayesian inference.

2022.08.05

IOT技術:IOT Technologyアルゴリズム:Algorithmsベイズ推定地理空間情報処理推論技術:inference Technology時系列データ解析機械学習:Machine Learning深層学習:Deep Learning

Dreams, Brain and Machine Learning From Dream Theory to Dream Data Science

Confirmation of dream experience in sleep (REM and non-REM sleep) using dream theory (Freud, Hobson activation-synthesis hypothesis, Ripley, etc.), brain networks and fMRI and machine learning (support Vectonema machine, Bayesian linear model with sparsity introduced)

2022.08.04

アルゴリズム:Algorithms哲学:philosophy心理学:psychology機械学習:Machine Learning深層学習:Deep Learning