Pronóstico de Series Temporales con Redes Neuronales en Python — Aprende Machine Learning

En el artículo de hoy veremos qué son las series temporales y cómo predecir su comportamiento utilizando redes neuronales con Keras y Tensorflow. Repasaremos el código completo en Python y la descarga del archivo csv del ejercicio propuesto con los datos de entrada. ¿Qué es una serie temporal y qué tiene de especial? Una serie…

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Machine Learning Basics – Gradient Boosting & XGBoost — R-bloggers

(This article was first published on Shirin’s playgRound, and kindly contributed to R-bloggers) In a recent video, I covered Random Forests and Neural Nets as part of the codecentric.ai Bootcamp. In the most recent video, I covered Gradient Boosting and XGBoost. You can find the video on YouTube and the slides on slides.com. Both are…

via Machine Learning Basics – Gradient Boosting & XGBoost — R-bloggers

¿Cómo funcionan las Convolutional Neural Networks? Visión por Ordenador — Aprende Machine Learning

En este artículo intentaré explicar la teoría relativa a las Redes Neuronales Convolucionales (en inglés CNN) que son el algoritmo utilizado en Aprendizaje Automático para dar la capacidad de “ver” al ordenador. Gracias a esto, desde apenas 1998, podemos clasificar imágenes, detectar diversos tipos de tumores automáticamente, enseñar a conducir a los coches autónomos y…

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Machine learning logistic regression for credit modelling in R — R-bloggers

(This article was first published on R Programming – DataScience+, and kindly contributed to R-bloggers) Categories Regression Models Tags Logistic Regression R Programming ROC Machine learning logistic regressions is a widely popular method to model credit modeling. There are excellent and efficient packages in R, that can perform these types of analysis. Typically you will…

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Recreating Michael Betancourt’s Bayesian modeling course from his online materials — R-bloggers

 

(This article was first published on Shravan Vasishth’s Slog (Statistics blog), and kindly contributed to R-bloggers)

Several people wanted to have the slides from Betancourt’s lectures at SMLP2018. It is possible to recreate most of the course from his writings:

1. Intro to probability:https://betanalpha.github.io/assets/case_studies/probability_theory.html

2. workflow:https://betanalpha.github.io/assets/case_studies/principled_bayesian_workflow.html

3. Diagnosis:https://betanalpha.github.io/assets/case_studies/divergences_and_bias.html

4. HMC: https://www.youtube.com/watch?v=jUSZboSq1zg

5. Validating inference: https://arxiv.org/abs/1804.06788 6. Calibrating inference:…

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Code for Workshop: Introduction to Machine Learning with R — R-bloggers

(This article was first published on Shirin’s playgRound, and kindly contributed to R-bloggers) These are the slides from my workshop: Introduction to Machine Learning with R which I gave at the University of Heidelberg, Germany on June 28th 2018. 3,305 more words

via Code for Workshop: Introduction to Machine Learning with R — R-bloggers