PyNeural: A simple but fast Python library for training neural networks
PyNeural is a neural network library written in Cython which is powered by a simple but fast C library under the hood. PyNeural uses the cblas library to perform the backprogation algorithm efficiently on multicore processors. PyNeural exposes a simple Python API that plays nicely with NumPy, making it easy to for you to munge your data sets as needed and quickly use them to train a neural network for classifiction.
Introduction to Applied Econometrics With R
I came across a January post from David Smith at Revolution Analytics, in his Revolutions blog. It’s titled, An Introduction to Applied Econometrics With R, and it refers to a very useful resource that’s been put together by Bruno Rodrigues of the University of Strasbourg. It’s called Introduction to Programming Econometrics With R, and you can download it from here.
Turning Data Into Awesome With sqldf and pandasql
Both R and Python possess libraries for using SQL statements to interact with data frames. While both languages have native facilities for manipulating data, the sqldf and pandasql provide a simple and elegant interface for conducting tasks using an intuitive framework that’s widely used by analysts.
Parametric Inference: The Power Function of the Test
In Statistics, we model random phenomenon and make conclusions about its population. For example, in an experiment of determining the true heights of the students in the university. …
scikit-learn video #4: Model training and prediction with K-nearest neighbors
Video #4: Model training and prediction
• What is the K-nearest neighbors classification model?
• What are the four steps for model training and prediction in scikit-learn?
• How can I apply this pattern to other machine learning models?
A notebook interface for Spark: A web-based notebook that enables interactive data analytics. You can make beautiful data-driven, interactive and collaborative documents with SQL, Scala and more.
Dockerizing a Shiny App
After a long pause of more than four months, I am finally back to post here. Unfortunately, many commitments prevented me keep posting, but coming back, i changed the deployment (now this blog runs entirely within a docker container with some other cool things I intend to post more forward) and wrote this post.
How large vectors in R might be stored compactly
Vectors in R can currently have elements of two sizes — 8-byte double-precision floating-point elements for `numeric’ vectors, or 4-byte elements for `integer’ or `logical’ vectors. You can also have vectors whose elements are 1-byte `raw’ values, but these raw vectors don’t support negative numbers, or NA values, so they aren’t suitable for general use.