微分積分:Calculus

Protected: MCMC method for calculating stochastic integrals: Algorithms other than Metropolis method (Gibbs sampling, MH method)

An overview of MCMC using Gibbs sampling and MH methods for probability integral computation for digital transformation and artificial intelligence task applications.

2021.11.30

微分積分:Calculus機械学習:Machine Learning確率・統計:Probability and Statistics自然言語処理:Natural Language Processing

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

Protected: Generative Deep Learning with Python and Keras (1) Text generation using LSTM

Text-generating DNN using LSTM with python/keras for digital transformation and artificial intelligence tasks

2021.11.19

python微分積分:Calculus最適化:Optimization機械学習:Machine Learning深層学習:Deep Learning自然言語処理:Natural Language Processing

Protected: Advanced deep learning with Python and Keras (3) Model optimization methods

Optimizing networks for deep learning with python/keras for digital transformation and artificial intelligence tasks

2021.11.18

python微分積分:Calculus最適化:Optimization機械学習:Machine Learning深層学習:Deep Learning

This is a good introduction to deep learning (Machine Learning Startup Series)Reading Notes

Overview of deep learning for digital transformation and artificial intelligence tasks, including machine learning, gradient descent, regularization, error back propagation, self-encoders, convolutional neural networks, recurrent neural networks, Boltzmann machines, and reinforcement learning.

2021.11.15

微分積分:Calculus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線形代数:Linear Algebra

Protected: Introduction to deep learning with Python and Keras (1) Overview of how to use Keras

Overview of Keras, a Python platform for implementing deep learning for digital transformation and artificial intelligence task utilization.

2021.10.30

python微分積分:Calculus最適化:Optimization機械学習:Machine Learning深層学習:Deep Learning

Introduction to Optimization Problems Combining Cone Optimization, Integer Optimization, and Network Models Problem Solving with Python Series

Overview of optimization techniques in machine learning using python for digital transformation (DX) and artificial intelligence (AI) tasks.

2021.10.24

python微分積分:Calculus推論技術:inference Technology機械学習:Machine Learning確率・統計:Probability and Statistics線形代数:Linear Algebra

Probabilistic approaches in machine learning

Probabilistic Generative Models Used in Digital Transformation (DX), Artificial Intelligence (AI), and Machine Learning (ML)

2021.07.22

ベイズ推定微分積分:Calculus最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics

Protected: Submodular Optimization and Machine Learning – Overview

Overview of inferior modular optimization, which is machine learning for discrete variables used in sensor placement optimization.

2021.05.13

微分積分:Calculus最適化:Optimization機械学習:Machine Learning確率・統計:Probability and Statistics

Word2Vec

An overview of Wprd2Vec, an application of deep learning models to natural language processing.

2021.05.07

微分積分:Calculus最適化:Optimization機械学習:Machine Learning深層学習:Deep Learning