digital transformation

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

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

Protected: Advanced deep learning with Python and Keras (1) Complex networks using the Keras Functional API

Construction of complex network models using Keras functional API with python/keras for digital transformation and artificial intelligence tasks (for multimodal problems, etc.)

2021.11.13

python最適化:Optimization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

Protected: DNNs for text and sequences with python and Keras (4) Sequence processing with bidirectional RNNs and CNNs

Bidirectional RNN and CNN application to sequence data in python/keras for digital transformation and artificial intelligencetasks.

2021.11.12

python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: DNN for text and sequences with python and Keras (3) Advanced use of recurrent neural networks(GRU)

Analysis of sequence data by GRU with pyhton/keras used for digital transformation and artificial intelligence tasks and improvement by recurrent dropout and recurrent layer stacking

2021.11.11

python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