Generating Images with Just Noise using GANs
GAN stands for Generative Adversarial Network, which is essentially applying game theory and putting a couple of artificial neural networks to compete with each other while they are trained at the same time. One network tries to generate the image and the other tries to detect if it is... Read more
Best Practices for Dealing with Concept Drift
You trained a machine learning model, validated its performance across several metrics which are looking good, you put it in production, and then something unforeseen happened (a pandemic like COVID-19 arrived) and the model predictions have gone crazy. Wondering what happened? You fell victim to a phenomenon called concept drift.... Read more
Modeling Regression Trees
Decision Trees (DTs) are probably one of the most popular Machine Learning algorithms. In my post “The Complete Guide to Decision Trees”, I describe DTs in detail: their real-life applications, different DT types and algorithms, and their pros and cons. I’ve detailed how to program Classification Trees, and now it’s... Read more
Image Detection as a Service
Across our two brands, Badoo and Bumble, we have over 500 million registered users worldwide uploading millions of photos a day to our platform. These images provide us with a rich data set from which we derive a wealth of insights. User Profile Information Within our data science team,... Read more
Upcoming Live Training: NLP Fundamentals with Leonardo De Marchi
I had the pleasure of presenting at eight ODSC events so far. Every time, there is something special on the trip: San Francisco, Boston, London, and so on. Eventually, I started understanding these cities bit by bit, conference by conference, restaurant by restaurant. But what is really special in... Read more
Modeling Classification Trees
Decision trees (DTs) are one of the most popular algorithms in machine learning: they are easy to visualize, highly interpretable, super flexible, and can be applied to both classification and regression problems. DTs predict the value of a target variable by learning simple decision rules inferred from the data... Read more
Enhancing Discovery in Data Science Through Novelty in Machine Learning
Note: Kirk will present two training sessions at the ODSC Europe 2020 Virtual Conference. One will focus on “Solving the Data Scientist’s Dilemma: the Cold-Start Problem with 10+ Machine Learning Examples” and the other will look at “Atypical Applications of Typical Machine Learning Algorithms.” I have always appreciated the... Read more
Machine Learning: Active Failures and Latent Conditions
Machine learning and AI applications are advancing in increasingly critical domains such as medicine, aviation, banking, finances, and more.  These applications not only are shaping the way in which industries are operating, but also how people are interacting and using their platforms/technologies. That said, it is of fundamental importance... Read more
Deep Learning with TensorFlow 2 & PyTorch
Deep Learning with TensorFlow 2 & PyTorch I’m greatly honored to be leading the charge on ODSC’s exciting new AI+ Training platform, which brings ODSC’s world-leading ability to provide professional training to data scientists into the digital realm while nevertheless retaining an intimate and engaging experience for attendees.  My... Read more
Using the ‘What-If Tool’ to Investigate Machine Learning Models
In this era of explainable and interpretable Machine Learning, one merely cannot be content with simply training the model and obtaining predictions from it. To be able to really make an impact and obtain good results, we should also be able to probe and investigate our models. Apart from... Read more