A loss function in Machine Learning is a measure of how accurately your ML model is able to predict the expected outcome i.e the ground truth. The loss function will take two items as input: the output value of our model and the ground truth expected value. The output of … Read moreUnderstanding the 3 most common loss functions for Machine Learning Regression
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 … Read moreDeep Learning for Clinical Diagnostics
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 … Read moreParsing Structured Documents with Custom Entity Extraction
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) … Read moreDo I Have Enough Money To Retire?
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 … Read moreFGO StyleGAN: This Heroic Spirit Doesn’t Exist
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 … Read moreUsing Bayesian Games to Address the Exploration-Exploitation Dilemma in Deep Learning Systems
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 … Read moreExploring Airbnb prices in London: which factors influence price?
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 … Read moreLearning to play snake at 1 million FPS
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 … Read moreQA: Machine Learning & Data Science Hackathons
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 … Read moreBook Recommender Engines