Here’s another post I co-authored with Chris McCormick on how to quickly and easily create a SOTA text classifier by fine-tuning BERT in PyTorch. It’s incredibly useful to take a look at this transfer learning approach if you’re interested in creating a high performance NLP model.
Please check out the post I co-authored with Chris McCormick on BERT Word Embeddings here. In it, we take an in-depth look at the word embeddings produced by BERT, show you how to create your own in a Google Colab notebook, and tips on how to implement and use these embeddings in your production pipeline. Check it out!
In a previous post we looked at root-finding methods for single variable equations. In this post we’ll look at the expansion of Quasi-Newton methods to the multivariable case and look at one of the more widely-used algorithms today: Broyden’s Method.
How do you find the roots of a continuous polynomial function? Well, if we want to find the roots of something like:
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”