数学:Mathematics

アルゴリズム:Algorithms

Overview of Causal Forest and examples of application and implementation in R and Python

  Causal Forest Causal Forest is a machine learning model for estimating causal effects from observed d...
アルゴリズム:Algorithms

Overview of graph neural networks and examples of application and implementation in python

Graph Neural Networks A graph neural network (GNN) is a type of neural network for data with a graph struc...
python

Noise reduction and data cleansing in machine learning, interpolation of missing values

Noise reduction and data cleansing in machine learning, interpolation of missing values Overview Noise remova...
数学:Mathematics

Science Fiction Novel “Three Bodies,” the Three Bodies Problem, and Machine Learning Technology

Introduction I'm reading "three bodies" (santai)vol1, vol2, vol3. Three Bodies is a full-length science ...
アルゴリズム:Algorithms

Protected: Neural Networks as Applied Models of Bayesian Inference

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Protected: Logistic regression as an applied model of Bayesian inference

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Protected: Tensor Decomposition and Recommendation as Applied Models of Bayesian Inference

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Protected: Inference by Gibbs sampling in a topic model as an applied model of Bayesian inference.

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Protected: Overview of the topic model as an applied model of Bayesian inference and application of variational inference

Overview of topic models as applied Bayesian inference models for digital transformation, artificial intelligence, and machine learning tasks and application of variational inference variational inference algorithms, Dirichlet distribution, categorical distribution, LDA, topic models in multimedia
アルゴリズム:Algorithms

Protected: Hidden Markov model building and structured variational inference in Bayesian inference

Hidden Markov model building and structured variational inference (mini-batch, structured variational inference, fully decomposed variational inference, additional learning, underflow, message passing, exact inference algorithms, forward-backward algorithms, approximate distribution of parameters) in Bayesian inference for digital transformation, artificial intelligence, machine learning tasks.
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