# Category: Machine learning

## Image-to-image translation with pix2pix

Conditional GANs (cGANs) may be used to generate one type of object based on another – e.g., a map based on a photo, or a color video based on black-and-white....continue reading.

## Attention-based Image Captioning with Keras

Image captioning is a challenging task at intersection of vision and language. Here, we demonstrate using Keras and eager execution to incorporate an attention mechanism that allows the network to...continue reading.

## Attention-based Image Captioning with Keras

Image captioning is a challenging task at intersection of vision and language. Here, we demonstrate using Keras and eager execution to incorporate an attention mechanism that allows the network to...continue reading.

## How to create a sequential model in Keras for R

This tutorial will introduce the Deep Learning classification task with Keras. With focus on one-hot encoding, layer shapes, train & model evaluation.continue reading.

## T-SQL job title generator

While writing a sample random function in using T-SQL Server, I have remembered, why not write a job title generator for T-SQL domain only. You might have seen so called...continue reading.

## Learning Images with Keras

Introduction Teaching machines to handle image data is probably one of the most exciting tasks in our daily routine at STATWORX. Computer vision in general is a path to many...continue reading.

## Neural style transfer with eager execution and Keras

Continuing our series on combining Keras with TensorFlow eager execution, we show how to implement neural style transfer in a straightforward way. Based on this easy-to-adapt example, you can easily...continue reading.

## Neural style transfer with eager execution and Keras

Continuing our series on combining Keras with TensorFlow eager execution, we show how to implement neural style transfer in a straightforward way. Based on this easy-to-adapt example, you can easily...continue reading.

## Getting started with deep learning in R

Many fields are benefiting from the use of deep learning, and with the R keras, tensorflow and related packages, you can now easily do state of the art deep learning...continue reading.

## Getting started with deep learning in R

Many fields are benefiting from the use of deep learning, and with the R keras, tensorflow and related packages, you can now easily do state of the art deep learning...continue reading.

## Sample size and class balance on model performance

Analyzing the relationship between the sample size and how it impacts on the accuracy in a classification modelcontinue reading.

If you’ve ever shopped online (*cough* Amazon *cough*), you’ve probably experienced the “vacuum cleaner effect”. You carefully buy one expensive item (e.g. a vacuum cleaner) and then you receive dozens...continue reading.

## How to do linear regression in R

Linear regression. It’s a technique that almost every data scientist needs to know. Although machine learning and artificial intelligence have developed much more sophisticated techniques, linear regression is still a...continue reading.

## Generating images with Keras and TensorFlow eager execution

Generative adversarial networks (GANs) are a popular deep learning approach to generating new entities (often but not always images). We show how to code them using Keras and TensorFlow eager...continue reading.

## Generating images with Keras and TensorFlow eager execution

Generative adversarial networks (GANs) are a popular deep learning approach to generating new entities (often but not always images). We show how to code them using Keras and TensorFlow eager...continue reading.

## CodeR: an LSTM that writes R Code

Everybody talks about them, many people know how to use them, few people understand them: Long Short-Term Memory Neural Networks (LSTM). At STATWORX, with the beginning of the hype around...continue reading.

## A performance benchmark of Google AutoML Vision using Fashion-MNIST

Google AutoML Vision is a state-of-the-art cloud service from Google that is able to build deep learning models for image recognition completely fully automated and from scratch. In this post,...continue reading.

## BooST series I: Advantage in Smooth Functions

By Gabriel Vasconcelos and Yuri Fonseca Introduction This is the first of a series of post on the BooST (Boosting Smooth Trees). If you missed the first post introducing the...continue reading.

## BooST (Boosting Smooth Trees) a new Machine Learning Model for Partial Effect Estimation in Nonlinear Regressions

By Gabriel Vasconcelos and Yuri Fonseca   We are happy to introduce our new machine learning method called Boosting Smooth Trees (BooST) (full article here). This model was a joint...continue reading.

## 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...continue reading.