TensorFlow Estimators
The tfestimators package is an R interface to TensorFlow Estimators, a high-level API that provides implementations of many different model types including linear models and deep neural networks.continue reading.
Category: Machine learning
DART: Dropout Regularization in Boosting Ensembles
The dropout approach developed by Hinton has been widely employed in deep learnings to prevent the deep neural network from overfitting, as shown in https://statcompute.wordpress.com/2017/01/02/dropout-regularization-in-deep-neural-networks. In the paper http://proceedings.mlr.press/v38/korlakaivinayak15.pdf, the...continue reading.
TensorFlow v1.3 Released
The final release of TensorFlow v1.3 is now available. This release marks the initial availability of several canned estimators including DNNClassifier and DNNRegressor.continue reading.
TensorFlow v1.3 Released
The final release of TensorFlow v1.3 is now available. This release marks the initial availability of several canned estimators including DNNClassifier and DNNRegressor.continue reading.
Google Vision API in R – RoogleVision
Using the Google Vision API in R Utilizing RoogleVision After doing my post last month on OpenCV and face detection, I started looking into other algorithms used for pattern detection...continue reading.
Sparkling Water and Moving Data Around
Sparkling Water is an application to integrate H2O with Spark. Below is an example showing how to move the data around among Pandas DataFrame, H2OFrame, and Spark Dataframe. 1. Define...continue reading.
GLM with H2O in R
Below is an example showing how to fit a Generalized Linear Model with H2O in R. The output is much more comprehensive than the one generated by the generic R...continue reading.
H2O Benchmark for CSV Import
The importFile() function in H2O is extremely efficient due to the parallel reading. The benchmark comparison below shows that it is comparable to the read.df() in SparkR and significantly faster...continue reading.
Finer Monotonic Binning Based on Isotonic Regression
In my early post (https://statcompute.wordpress.com/2017/01/22/monotonic-binning-with-smbinning-package/), I wrote a monobin() function based on the smbinning package by Herman Jopia to improve the monotonic binning algorithm. The function works well and provides...continue reading.
Joining Tables in SparkR
library(SparkR, lib.loc = paste(Sys.getenv("SPARK_HOME"), "/R/lib", sep = "")) sc <- sparkR.session(master = "local") df1 <- read.df("nycflights13.csv", source = "csv", header = "t…continue reading.
Taking Advanced Analytics to the Cloud – Part I: R on AWS
Running R on the cloud isn’t very difficult. This demo shows how to get Rstudio running on Amazon Web Services. To run R on the cloud we need to initiate...continue reading.
Taking Advanced Analytics to the Cloud – Part II: Objects from S3 to R using the aws.s3 package
When taking advanced analytics to the cloud you’ll need a strong understanding of your platform – whether it’s compute, storage, or some other feature. This tutorial walks you through reading...continue reading.
Data Aggregation with PySpark
Import CSV File into Spark Dataframe Data Aggregation with Spark Dataframe Data Aggregation with Spark SQLcontinue reading.
Random Search for Optimal Parameters
Practices of manual search, grid search, or the combination of both have been successfully employed in the machine learning to optimize hyper-parameters. However, in the arena of deep learning, both...continue reading.
A Simple Convolutional Neural Network for The Binary Outcome
Since CNN(Convolutional Neural Networks) have achieved a tremendous success in various challenging applications, e.g. image or digit recognitions, one might wonder how to employ CNNs in classification problems with binary...continue reading.
Playing with dimensions: from Clustering, PCA, t-SNE… to Carl Sagan!
Explore the intersection of concepts like dimension reduction, clustering, data preparation, PCA, HDBSCAN, k-NN, SOM, deep learning…and Carl Sagan!continue reading.
Naive Bayes Classification in R (Part 2)
Following on from Part 1 of this two-part post, I would now like to explain how the Naive Bayes classifier works before applying it to a classification problem involving breast...continue reading.
Naive Bayes Classification in R (Part 1)
Introduction A very useful machine learning method which, for its simplicity, is incredibly successful in many real world applications is the Naive Bayes classifier. I am currently taking a machine...continue reading.
