Digests » 55
The Global Financial Crisis (hereafter, GFC) of 2007–2008 had far reaching financial and legal consequences, affecting millions of livelihoods. Sparked by the proliferation of subprime mortgages and exemplified by fall of Lehman Brothers, it’s aftershocks were widely felt around the world. Following a period of recovery and growth, the world plunged into the European Sovereign Debt Crisis (hereafter, ESDC) beginning in late 2009, the effects of which have been argued to be still ongoing today. As global markets tend to operate in a cyclical fashion, scenario planning for the next financial crisis is not a matter of if, but when. Indeed, a Google search would lead to hundreds of differing opinions on the matter, from conclusions based on quantitative metrics to others based on the prophecies of Nostrodamus.
Imagine you’re sleeping, and you hear strange noises in your front lawn. You’re very sleepy, so you hypothesize that the strange noises are being generated by a hungry dinosaur. You think to yourself, ‘this is exactly what I would hear if there was a dinosaur outside in my front lawn’. But then as you think more about it, you realize that the likelihood of there actually being a dinosaur in your front lawn is extremely low; whereas the likelihood of hearing strange noises from the front lawn is likely pretty high. So you exhale as you realize that the actual probability of there being a dinosaur in your front lawn, aka your original hypothesis, given the evidence is extremely low.
Have you ever wondered what a Gyarados would look like as a fire type? Or grass type, or electric type? For my last project at the Recurse Center, I trained CycleGAN, an image-to-image translation model, on images of Pokémon of different types.
Do you have a feeling that wherever you turn someone is talking about artificial intelligence? Terms like machine learning, artificial neural networks and reinforcement learning, became these big buzzwords that you cannot escape from. This hype might be bigger than the one we faced with micro-services and serverless a couple of years ago. When we are talking about these techniques we are usually talking about algorithms and architectures that we will apply to our data. Sometimes we talk about math (linear algebra, probability, calculus, etc.) that can lead us to the solution for our problem.
A curated list of applied machine learning and data science notebooks and libraries accross different industries. The code in this repository is in Python (primarily using jupyter notebooks) unless otherwise stated. The catalogue is inspired by awesome-machine-learning.