Batch normalization has a simple goal: stabilize the gradients of large computational graphs. In doing so, this technique has enabled the deep learning renaissance that almost every major ML breakthrough in the last 5 years has relied on. The concept is sound: by regularizing the mean and variance of the inputs of nearly every layer…
Author: jbetker
Diving into Super Resolution
After finishing my last project, I wanted to understand generative networks a bit better. In particular, GANs interest me because there doesn’t seem to be much research on them going on in the language modeling space. To build up my GAN chops, I decided to try to figure out image repair and super-resolution. My reasoning…
Fine-tuning XLNet For Generation Tasks
About a month ago, I decided to take the plunge into learning how to fine tune a language generation model. One use-case of language generation that I found particularly compelling was abstractive document summarization. A lot of the papers currently available that deal with abstractive summarization and transformers work by truncating the input text to…
Learning to Learn: My Second Foray Into Machine Learning
My desire to understand how the mind works started when I was choosing what I wanted to do in college, in 2000. Back then I was a nerdy kid who was pretty good with computers, but who had grown an insatiable interest for figuring out how the mind ticked. Not knowing a whole lot about…