Blog Archives

Linear programming in R

August 16, 2018
By
Linear programming in R

Linear programming is a technique to solve optimization problems whose constraints and outcome are represented by linear relationships. Simply put, linear programming allows to solve problems of the following kind: Maximize/minimize $\hat C^T \hat X$ Under the constraint $\hat A \hat X \leq \hat B$ And the constraint $\hat X \geq 0$ This doesn’t seem much when you glance at it but in practice...

Read more »

PCA revisited: using principal components for classification of faces

August 13, 2018
By
PCA revisited: using principal components for classification of faces

This is a short post following the previous one (PCA revisited). In this post I’m going to apply PCA to a toy problem: the classification of faces. Again I’ll be working on the Olivetti faces dataset. Please visit the previous post PCA revisited to read how to download it. The goal of this post is to fit a simple classification model...

Read more »

PCA revisited

August 12, 2018
By
PCA revisited

Principal component analysis (PCA) is a dimensionality reduction technique which might come handy when building a predictive model or in the exploratory phase of your data analysis. It is often the case that when it is most handy you might have forgot it exists but let’s neglect this aspect for now ;) I decided to write this post mainly for...

Read more »

Resizing spatial data in R

March 5, 2017
By
Resizing spatial data in R

Here I am after a short break, writing again about R!In december I worked on a project that required me to work on spatial data. This led me to learn about how R deals with this kind of data and to look around for ways to make my “spatial data experience” less painful. I discovered that R is, as...

Read more »

7th MilanoR meeting + talks live streaming

October 25, 2016
By

On 27th of October I’m going to attend the 7th MilanoR meeting featuring the following two talks: 1. Interactive big data analysis with R: SparkR and MongoDB: a friendly walkthrough  by  Thimoty Barbieri and Marco Biglieri 2. Power consumption prediction based on statistical learning techniques by Davide Pandini This is my first official R event and I’m very much...

Read more »

My first Shiny App: control charts

September 24, 2016
By
My first Shiny App: control charts

After having carefully followed the online official Shiny tutorial, I decided to make a quick try at making my very first Shiny App. I should say that I found myself very well with the explanation given and Shiny was definitely one of the libraries tha...

Read more »

Image recognition tutorial in R using deep convolutional neural networks (MXNet package)

August 5, 2016
By
Image recognition tutorial in R using deep convolutional neural networks (MXNet package)

This is a detailed tutorial on image recognition in R using a deep convolutional neural network provided by the MXNet package. After a short post I wrote some times ago I received a lot of requests and emails for a much more detailed explanation, therefore I decided to write this tutorial. This post will show a reproducible example on...

Read more »

Plain vanilla recurrent neural networks in R: waves prediction

August 5, 2016
By
Plain vanilla recurrent neural networks in R: waves prediction

While continuing my study of neural networks and deep learning, I inevitably meet up with recurrent neural networks. Recurrent neural networks (RNN) are a particular kind of neural networks usually very good at predicting sequences due to their inner w...

Read more »

Image recognition in R using convolutional neural networks with the MXNet package

July 25, 2016
By
Image recognition in R using convolutional neural networks with the MXNet package

Among R deep learning packages, MXNet is my favourite one. Why you may ask? Well I can’t really say why this time. It feels relatively simple, maybe because at first sight its workflow looks similar to the one used by Keras, maybe because it was my f...

Read more »

Image recognition in R using convolutional neural networks with the MXNet package

July 25, 2016
By
Image recognition in R using convolutional neural networks with the MXNet package

Among R deep learning packages, MXNet is my favourite one. Why you may ask? Well I can’t really say why this time. It feels relatively simple, maybe because at first sight its workflow looks similar to the one used by Keras, maybe because it was my f...

Read more »

Search R-bloggers


Sponsors

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)