AI

C言語

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
推論技術:inference Technology

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
python

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
python

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
python

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
Symbolic Logic

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
微分積分:Calculus

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
python

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
python

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
python

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
次のページ
タイトルとURLをコピーしました