Brand Voice: Deep Learning for Speech Synthesis
The production of artificial natural-sounding human speech is a fascinating topic due to its complexity and surprising results, with applications that range from chatbots to the automatization of audio content in news media. One obvious example of a Text-to-Speech (TTS) application for news media is a... Read more
Fake News: Where Do We Stand in the Fight Against Disinformation
If you’re familiar with conspiracy theories regarding COVID-19 vaccines, Barack Obama’s birthplace, or Hillary Clinton’s ties to “Pizzagate”, you’ve experienced only a fraction of contemporary disinformation. With the rise of social media and its capacity for viral posts and fake news, disinformation campaigns have become a common... Read more
PyTorch Lightning: From Research to Production, Minus the Boilerplate
The following post introduces PyTorch Lightning, outlines its core design philosophy, and provides inline examples of how this philosophy enables more reproducible and production-capable deep learning code. What is PyTorch Lightning? PyTorch Lighting is a lightweight PyTorch wrapper for high-performance AI research. Simply put, PyTorch Lightning... Read more
Why TensorFlow Will Stand Out on Your Resume in 2021
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
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 the power of transfer learning and explainable AI.... 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