DropConnect Implementation in Python and TensorFlow

I wouldn’t expect DropConnect to appear in TensorFlow, Keras, or Theano since, as far as I know, it’s used pretty rarely and doesn’t seem as well-studied or demonstrably more useful than its cousin, Dropout. However, there don’t seem to be any implementations out there, so I’ll provide a few ways of doing so. Continue reading “DropConnect Implementation in Python and TensorFlow”

Style Transfer with Tensorflow

A Neural Algorithm of Artistic Style” is an accessible and intriguing paper about the distinction and separability of image content and image style using convolutional neural networks (CNNs). In this post we’ll explain the paper and then run a few of our own experiments.

To begin, consider van Gogh’s “The Starry Night”: Continue reading “Style Transfer with Tensorflow”

The Box-Cox Transformation

The Box-Cox transformation is a family of power transform functions that are used to stabilize variance and make a dataset look more like a normal distribution. Lots of useful tools require normal-like data in order to be effective, so by using the Box-Cox transformation on your wonky-looking dataset you can then utilize some of these tools.

Here’s the transformation in its basic form. For value x and parameter \lambda:

\displaystyle \frac{x^{\lambda}-1}{\lambda} \quad \text{if} \quad x\neq 0 

\displaystyle log(x) \quad \text{if} \quad x=0

Continue reading “The Box-Cox Transformation”

Decorators and Metaprogramming in Python


Decorators are intuitive and extremely useful. To demonstrate, we’ll look at a simple example. Let’s say we’ve got some function that sums all numbers 0 to n:

def sum_0_to_n(n):
    count = 0
    while n > 0:
        count += n
        n -= 1
    return count

and we’d like to time the performance of this function. Of course we could just modify the function like so:

Continue reading “Decorators and Metaprogramming in Python”

Shallow Parsing for Entity Recognition with NLTK and Machine Learning

Getting Useful Information Out of Unstructured Text

Let’s say that you’re interested in performing a basic analysis of the US M&A market over the last five years. You don’t have access to a database of transactions and don’t have access to tombstones (public advertisements announcing the minimal details of a closed deal, e.g. ABC acquires XYZ for $500mm). What you do have is access to is a large corpus of financial news articles that contain within them – somewhere – the basic transactional details of M&A deals.

What you need to do is design a system that takes in this large database and outputs clean fields containing M&A transaction details. In other words, map an excerpt like this: Continue reading “Shallow Parsing for Entity Recognition with NLTK and Machine Learning”