**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?

**Apache Zeppelin**

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.

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