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Deep Learning for Clinical Diagnostics

Image source Making safer biomedical predictions with Deep Learning This is the fourth article in the series Deep Learning for Life Sciences. In the previous posts, I showed how to use Deep Learning on Ancient DNA, Deep Learning for Single Cell Biology and Deep Learning for Data Integration. Now we are

Parsing Structured Documents with Custom Entity Extraction

Let’s talk about parsing structured documents with entity extraction! There are lots of great tutorials on the web that explain how to classify chunks of text with Machine Learning. But what if, rather than just categorize text, you want to categorize individual words, like this: You can’t make me apologize

Do I Have Enough Money To Retire?

Assumptions of Our Example Our client, Randy Let’s pretend that we work for a financial firm called Lazy Advisors, LLC. One of our relatively more financially savvy clients, Randy, has come in for his retirement assessment. Here are his stats: Gender & Age: Male, 55 Marital Status: Single (and ready to mingle)

FGO StyleGAN: This Heroic Spirit Doesn’t Exist

Sample outputs from FGO StyleGAN. Also here is a the link to view it for free When I first saw Nvidia’s StyleGAN’s results I felt like it looked like a bunch of black magic. I am not as experienced in the area of GANs as other parts of deep learning, this

Using Bayesian Games to Address the Exploration-Exploitation Dilemma in Deep Learning Systems

Artificial intelligence(AI) agents often operate in environments with partial or incomplete information. In those settings, agents are often forced to find a balance between exploring the environment or taking actions that yield an immediate reward. The exploration-exploitation dilemma is one of the fundamental frictions in modern AI systems particularly in

Exploring Airbnb prices in London: which factors influence price?

Project aims and background Airbnb is a home-sharing platform that allows home-owners and renters (‘hosts’) to put their properties (‘listings’) online, so that guests can pay to stay in them. Hosts set their own prices for their listings, and although Airbnb and other sites provide some general guidance, there are

Learning to play snake at 1 million FPS

There’s a bit more logic required such as checking for collisions and food collection, resetting environments after death and checking snakes aren’t trying to move backwards — it was very fun thinking of ways to implement all this in a way that is vectorized across multiple environments. The end result of all

QA: Machine Learning & Data Science Hackathons

This is the final hackathon trilogy part. The first part was devoted to the motivation to participate in such events. The second part told about the organizer’s mistakes and their results. The final part provides answers to all the remaining questions, which wasn’t fit in the previous two parts. Tell

Book Recommender Engines

I decided to build a couple recommender engines so I could better explore the behind the scenes for how they operate. I built two engines that each recommend books. One is collaborator-based and the other is content-based. PART I: COLLABORATOR ENGINE With collaborator engines, recommendations are based on how users

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