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”
“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”
How many different ways can we multiply the elements of a variable-length list in Python? Continue reading “Flexible Python: Product of a List”
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 and parameter :
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:
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”
A useful snippet for visualizing decision trees with pydotplus. It took some digging to find the proper output and viz parameters among different documentation releases, so thought I’d share it here for quick reference.