深層学習:Deep Learning

Protected: Advanced online learning (3) Application to deep learning (mini-batch stochastic gradient descent, momentum method, accelerated gradient method)

Improving computational efficiency by applying mini-batch stochastic gradient descent, momentum, and accelerated gradient methods to deep learning for digital transformation , artificial intelligence , and machine learning tasks.

2022.01.12

オンライン学習微分積分:Calculus最適化:Optimization機械学習:Machine Learning深層学習:Deep Learning確率・統計:Probability and Statistics

Machine Learning Professional Series “Reinforcement Learning” Reading Memo

A reference book on reinforcement learning to observe the current situation and determine what action to take, used in digital transformation ,artificial intelligence , and machine learning tasks.

2022.01.10

機械学習:Machine Learning深層学習:Deep Learning

Machine Learning Professional Series “Online Machine Learning” Reading Memo

Online learning reference books used for digital transformation , artificial intelligence , and machine learning tasks such as sequential processing of large-scale data.

2022.01.08

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

Protected: MCMC method for calculating stochastic integrals: Algorithms other than Metropolis method (HMC method)

Algorithm and C implementation of the Hybrid Monte Calro method applied to complex stochastic integral calculations for digital transformation and artificial intelligence tasks.

2021.11.29

C言語深層学習:Deep Learning確率・統計:Probability and Statistics自然言語処理:Natural Language Processing

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

Protected: Advanced deep learning with Python and Keras (2) Model monitoring using Keras callbacks and TensorBord

Monitoring of networks in deep learning process using pyton/keras for digital transformation and artificial intelligence tasks (monitoring of models using Keras callbacks and TensorBord)

2021.11.17

python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

Comparison of tensorflow, Keras and pytorch

Comparison of tensorflow, keras, and pytorch, deep learning frameworks used for digital transformation and artificial intelligence tasks

2021.11.14

python機械学習:Machine Learning深層学習:Deep Learning

Protected: Evolving Deep Learning with PyTorch(OpenPose, SSD, AnoGAN, Efficient GAN, DCGAN, Self-Attention GAN, BERT, Transformer, GAN, PSPNet, 3DCNN, ECO)

OpenPose, SSD, AnoGAN, Efficient GAN, DCGAN, Self-Attention GAN, BERT, Transformer, GAN using pytorch for Digital Transformation and Artificial Intelligence tasks, PSPNet,3DCNN,ECO and other advanced DNNs

2021.11.14

python機械学習:Machine Learning深層学習:Deep Learning画像認識技術自然言語処理:Natural Language Processing音声信号認識技術