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Accelerating Deep Learning Recommender Systems by Over 15x Using RAPIDS, PyTorch, and fast.ai
This June, the RAPIDS Deep Learning team took part in the RecSys 2019 Challenge, where we placed 15th out of 1534 teams despite joining the competition in its final weeks. The competition centered around recommending hotel listings to users of the website Trivago, who was the host of the competition. Given a... Read more
What Your Business Needs to Know About Data Variety
The bigger your business, the more likely your data is…interesting. Old methods of data collection put data in silos across several departments. The computing power to handle data in all its forms wasn’t there, so breaking it up in the name of agile operations was key. Now, we’ve got... Read more
Deep Learning for Third-Party Risk Identification and Evaluation
Editor’s Note: Learn more about the technical details of this article at the talk “Deep Learning for Third-Party Risk Identification and Evaluation at Dow Jones” at ODSC Europe 2019 For more than 17 years, Dow Jones has supplied risk and compliance data to banking and financial institutions, corporations and... Read more
Opening The Black Box—Interpretability In Deep Learning
Editor’s Note: See Joris and Matteo at their tutorial “Opening The Black Box — Interpretability in Deep Learning” at ODSC Europe 2019 this November 20th in London. Why interpretability?  In the last decade, the application of deep neural networks to long-standing problems has brought a breakthrough in performance and... Read more
What is Implicit Deep Learning?
Editor’s note: Laurent is a speaker for the upcoming ODSC West in California later this year! Be sure to attend his talk there. See a larger version of the cover image here. Prediction rules in deep learning are based on a forward, recursive computation through several layers. Implicit deep... Read more
Watch: Deep Learning in Real Time
Deep learning provides (relatively) new methods of automating a wide array of tasks historically thought only to be accomplished by the human brain. Applications range from difficult classifications and regressions to natural language understanding and image recognition. With Tyler Freckmann, we will take a tour of different deep learning... Read more
Using Mobile Devices for Deep Learning
A key avenue for deploying deep learning models is a mobile device. The advantage of running models in mobile apps instead of sending them to the cloud is the reduction in latency and the ability to ensure data privacy for users. Despite the variety of deep learning libraries and... Read more
Using RAPIDS with PyTorch
In this post we take a look at how to use cuDF, the RAPIDS dataframe library, to do some of the preprocessing steps required to get the mortgage data in a format that PyTorch can process so that we can explore the performance of deep learning on tabular data and... Read more
How to Use Deep Learning to Write Shakespeare
LSTM recurrent neural networks can be trained to generate free text. Let’s see how well AI can imitate the Bard “Many a true word hath been spoken in jest.” ― William Shakespeare, King Lear   “O, beware, my lord, of jealousy; It is the green-ey’d monster, which doth mock The... Read more
Visualizing Your Convolutional Neural Network Predictions With Saliency Maps
In many cases, understanding why the model predicted a given outcome is a key detail for model users and a necessary diagnostic to insure your model makes decisions based on the correct features. For example, if you built a convolutional neural network that performed well at predicting damaged products... Read more