人工知能:Artificial Intelligence

Making Logic Part 4 – Logic is Interesting from Here Non-Classical Logic Reading Notes

Fundamentals of logic (non-classical logic, multi-valued logic, intuitionistic logic, and haphazard logic) in mathematical logic used for reasoning mechanisms in artificial intelligence tasks.

2021.11.21

推論技術:inference Technology数理論理学:Mathematical logic

Creating Logic, Part 3 – Another Look at Logic Reading Notes

Foundations of logic (deductive reasoning, syntax and semantics) in mathematical logic used for reasoning mechanisms in artificial intelligence tasks.

2021.11.20

推論技術:inference Technology数理論理学:Mathematical logic

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

Creating Logic, Part 2: Expanding Logic Reading Notes

Fundamentals of logic (predicate logic and tableau, PPL, IPL) in mathematical logic utilized for reasoning mechanisms in artificial intelligence tasks.

2021.11.16

Symbolic Logic推論技術:inference Technology数理論理学:Mathematical logic集合論:Set theory

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