python Overview of the trace norm and related algorithms and implementation examples
Trace norm overview
The trace norm (or nuclear norm) is a type of matrix norm, which can be defined as...
幾何学:Geometry
python python Overview of the finite element method and examples of algorithms and implementations.
Overview of the finite element method.
The Finite Element Method (FEM) is a method for numerically analy...
アルゴリズム:Algorithms Overview of Group Regularization with Duplicates and Examples of Implementations
Overview
Overlapping group regularization (Overlapping Group Lasso) is a type of regularization method...
幾何学:Geometry Cross-Entropy Loss
Overview of Cross-Entropy Loss
Cross-Entropy Loss (Cross-Entropy Loss) is one of the common loss functions use...
アルゴリズム:Algorithms Protected: Model Building and Inference in Bayesian Inference – Overview and Models of Hidden Markov Models
Model building and inference of Bayesian inference for digital transformation, artificial intelligence, and machine learning tasks - Overview of hidden Markov models and models eigenvalues, hyperparameters, conjugate prior, gamma prior, sequence analysis, gamma distribution, Poisson distribution, mixture models graphical model, simultaneous distribution, transition probability matrix, latent variable, categorical distribution, Dirichlet distribution, state transition diagram, Markov chain, initial probability, state series, sensor data, network logs, speech recognition, natural language processing
アルゴリズム:Algorithms Protected: Neural Networks as Applied Models of Bayesian Inference
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アルゴリズム:Algorithms Protected: Logistic regression as an applied model of Bayesian inference
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アルゴリズム:Algorithms Protected: Tensor Decomposition and Recommendation as Applied Models of Bayesian Inference
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アルゴリズム:Algorithms Protected: Inference by Gibbs sampling in a topic model as an applied model of Bayesian inference.
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アルゴリズム:Algorithms 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