Why TensorFlow Will Stand Out on Your Resume in 2020
You have likely heard about TensorFlow in the machine & deep learning circles for quite a while now, and for good reason. This Google-developed framework excels where many other libraries don’t, such as with its scalable nature designed for production deployment. With that, here are just... Read more
Understanding the Temporal Difference Learning and its Predication 
The temporal difference learning algorithm was introduced by Richard S. Sutton in 1988.  The reason the temporal difference learning method became popular was that it combined the advantages of dynamic programming and the Monte Carlo method. But what are those advantages?  This article is an excerpt from the... Read more
Deep Learning-Driven Text Summarization & Explainability with Reuters News Data
Image credit: REUTERS/Dominic Ebenbichler for Reuters news data Editor’s note: At ODSC West 2020, Nadja Herger, Nina Hristozova, and Viktoriia Samatova will hold a workshop focused on text summarization and that will allow you to automatically generate news headlines powered by Reuters News, and learn about... Read more
Why I Love Keras and Why You Should Too
I started working with Deep Learning (DL) in the 2016 – 2017 time frame when the framework ecosystem was much more diverse and fragmented than it is today. Theano was the gold standard at the time, Tensorflow had just been released, and DeepLearning4j was still being... Read more
Active Learning: Why Some Data Are More Equal Than Others
This article discusses active learning and how it can help streamline the data annotation process. Artificial Intelligence is a technology that thrives on two kinds of fuel – computing power and data. Their increasing affordability is the driving force behind the recent AI boom. In fact,... Read more
Introduction to Bayesian Deep Learning
Bayes’ theorem is of fundamental importance to the field of data science, consisting of the disciplines: computer science, mathematical statistics, and probability. It is used to calculate the probability of an event occurring based on relevant existing information. Bayesian inference meanwhile leverages Bayes’ theorem to update... Read more
Enhancing Satellite Imagery Through Super-Resolution
In order to accurately locate crop fields from satellite imagery, it is conceivable that images of a certain quality are required. Although deep learning is notoriously known for being able to pull off miracles, we human beings will have a real field day labeling the data... Read more
Recurrent Neural Networks in the Cloud and Edge
Traditionally, neural networks had all their inputs and outputs independent of each other, but in cases, for instance, where it is required to predict the next word of a sentence, information about previous words is essential. Thus, RNN came into existence. Recurrent Neural Network (RNN) is a... 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. My talk focuses on image... Read more
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 Deep Learning with TensorFlow... Read more