Thursday, April 7, 2016

Top 10 Deep Learning Tools for R/Python/Java/Matlab/C++

Top 10 Deep Learning Tools for R/Python/Java/Matlab/C++


Deep Learning Tools For R

H2O - Parallel distributed machine learning algorithms such as generalized linear models, gradient boosting machines, random forests, and neural networks (deep learning) within various cluster environments

Deep Learning Tools For R and Python

MXNET - Deep learning framework designed for both efficiency and flexibility. It allows you to mix the flavours of deep learning programs together to maximize efficiency and your productivity.

Neuraldesigner - Neural Designer is an innovative deep learning tool for predictive analytics. Deep learning algorithms are the most powerful method to discover intricate relationships, recognize complex patterns or predict current trends in your data.

Deep Learning Tools For Python

Theano - Allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently.

Caffe - Caffe is a deep learning framework made with expression, speed, and modularity in mind. matcaffe – is the caffe package in caffe/matlab in which you can integrate Caffe in your Matlab code.

TensorFlow - Provides a straightforward way for users to train computers to perform tasks by feeding them large amounts of data. The software incorporates various methods for efficiently building and training simulated “deep learning” neural networks across different computer hardware.

Deep Learning Tools For Java

DL4J - The first commercial-grade, open-source, distributed deep-learning library written for Java and Scala. Integrated with Hadoop and Spark, DL4J is designed to be used in business environments, rather than as a research too.

Deep Learning Tools For C++/Scripting

Opennn - It is intended for advanced users, with high C++ and machine learning skills.

Torch - Torch is a scientific computing framework with wide support for machine learning algorithms that puts GPUs first. It is easy to use and efficient, thanks to an easy and fast scripting language, LuaJIT, and an underlying C/CUDA implementation.

Elektronn - Is a deep learning toolkit that makes powerful Neural Networks accessible to scientists outside of the machine learning community. Written in Python and based on the Theano framework.


Thursday, March 31, 2016

taskscheduleR: Scheduling R script via R and Rstudio

Scheduling R script via R / Rstudio

Scheduling R script via R.?

taskscheduleR - It allow users to automate R script on specific timepoints from R / Rstudio itself. No need of windows Task scheduling or Linux Cron to automate R script.

Installation of taskscheduleR

devtools::install_github("jwijffels/taskscheduleR")

If you want the RStudio add-in to work, also install miniUI and shiny

install.packages('miniUI') install.packages('shiny')
Open Rscheduler window

Click the Addins menu added in Rstudio

You can also open Rscheuler window in Browser by

hit below command in R terminal,
taskscheduleR:::taskschedulerAddin()

you will notify with "Listening on http://127.0.0.1:5499 " and the same can be viewed in browser