This document provides an overview of recurrent neural networks (RNNs) and some of their applications. It discusses the basic structure of RNNs and how they can be used for tasks like sequence prediction and language modeling. It also introduces more advanced RNN architectures like LSTMs, GRUs, and encoder-decoder models that address some limitations of traditional RNNs. Examples of applications discussed include sequence-to-sequence learning, language translation, and recursive neural networks.
ベイズ機械学習(an introduction to bayesian machine learning)医療IT数学同好会 T/T
This document provides an introduction to Bayesian machine learning. It discusses key concepts like Bayes' theorem, the modeling and inference procedures in Bayesian learning, and examples like linear regression and Gaussian mixture models. It also introduces variational inference as a technique for approximating intractable posterior distributions. Finally, it lists some example papers and programming languages/libraries for probabilistic programming.
The document describes various probability distributions that can arise from combining Bernoulli random variables. It shows how a binomial distribution emerges from summing Bernoulli random variables, and how Poisson, normal, chi-squared, exponential, gamma, and inverse gamma distributions can approximate the binomial as the number of Bernoulli trials increases. Code examples in R are provided to simulate sampling from these distributions and compare the simulated distributions to their theoretical probability density functions.
This document provides an overview of recurrent neural networks (RNNs) and some of their applications. It discusses the basic structure of RNNs and how they can be used for tasks like sequence prediction and language modeling. It also introduces more advanced RNN architectures like LSTMs, GRUs, and encoder-decoder models that address some limitations of traditional RNNs. Examples of applications discussed include sequence-to-sequence learning, language translation, and recursive neural networks.
ベイズ機械学習(an introduction to bayesian machine learning)医療IT数学同好会 T/T
This document provides an introduction to Bayesian machine learning. It discusses key concepts like Bayes' theorem, the modeling and inference procedures in Bayesian learning, and examples like linear regression and Gaussian mixture models. It also introduces variational inference as a technique for approximating intractable posterior distributions. Finally, it lists some example papers and programming languages/libraries for probabilistic programming.
The document describes various probability distributions that can arise from combining Bernoulli random variables. It shows how a binomial distribution emerges from summing Bernoulli random variables, and how Poisson, normal, chi-squared, exponential, gamma, and inverse gamma distributions can approximate the binomial as the number of Bernoulli trials increases. Code examples in R are provided to simulate sampling from these distributions and compare the simulated distributions to their theoretical probability density functions.
Презентация для мастер-класса по разработке веб-сайтов на конференции РИФ Воронеж 3-4 сентября 2009 года.
Слайды — Александр Щедрин.
Доклдачики — Андрей Парфёнов, Виктория Логачева, Андрей Ещенко, Дмитрий Провоторов, Денис Семёнов, Константин Пилюгин.