fbpx
Generative Adversarial Networks for Finance
Financial instruments like options and futures have been around for more than two centuries. Although they became quite notorious during the 2008 stock market turmoil, they serve a real economic purpose for companies around the world. To explain financial... Read more
What You Need to Know about DeepMind’s BSuite
Imagine this. You know that reinforcement learning has been responsible for some of AI’s most significant advancements. You’re in the exploratory phase of implementing your first project. You’d love a way to evaluate whether your RL agent is appropriate for the task you have, something not always apparent without... Read more
A Crash Course on Deep Learning in the Cloud
Deep learning is the newest area of machine learning and has become ubiquitous in predictive modeling. The complex brain-like structure of deep learning models is used to find intricate patterns in large volumes of data. They have substantially improved the performance of general supervised models, time series, speech recognition,... Read more
Behavior Suite for Reinforcement Learning
A team from DeepMind Technologies—made up of Ian Osband, Yotam Doron, Matteo Hessel, John Aslanides, Eren Sezner, Andre Saraiva, Katrina McKinney, Tor Lattimore, Csaba Szepezvari, Satinder Singh, Benjamin Van Roy, Richard Sutton, David Silver, and Hado Van Hesselt—has recently published a piece on their new program Behavior Suite (bsuite... Read more
Sequence Modelling with Deep Learning
This is a short preview post for my upcoming tutorial  “Sequence Modelling with Deep Learning” at ODSC London in November 2019. — Much of data is sequential — think speech, text, DNA, stock prices, financial transactions, and customer action histories. Our best-performing methods for modelling sequence data use deep neural networks,... Read more
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
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
Deep Learning with TensorFlow 2.0
Editor’s Note: See Jon’s talk “Deep Learning with TensorFlow 2.0” at ODSC West 2019.  This summer, I had a blast speaking at Immersive A.I.—the first annual Open Data Science Conference (ODSC) event in New York. The venue was flawless, the organizers were exceptionally well-prepared, and there was a remarkable... Read more
Best Deep Reinforcement Learning Research of 2019 So Far
In this article, I’ve conducted an informal survey of all the deep reinforcement learning research thus far in 2019 and I’ve picked out some of my favorite papers. This list should make for some enjoyable summer reading!   As we... 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