Tensorflow and Deep Reinforcement Learning without a PhD

On the forefront of deep learning research is a technique called reinforcement learning, which bridges the gap between academic deep learning problems and ways in which learning occurs in nature in weakly supervised environments. This technique is heavily used when researching areas like learning how to walk, chase prey, navigate complex environments, and even play Go. In this session, Martin Görner will detail how a neural network can be taught to play the video game Pong from just the pixels on the screen. No rules, no strategy coaching, and no PhD required. Martin will build on this application to show how the approach can be generalized to other problems involving a non-differentiable steps that cannot be trained using traditional supervised learning techniques.

This is a prelude to Martin’s full day workshop on Thursday, June 20th: Hands-On Deep Learning in the Cloud: Fast and Lean Data Science with Tensorflow, Keras, and TPUs.

Safely Driven: How Trimble Transportation Mobility is Applying Deep Learning to Make the Roads Safer for Everyone

Deep learning certainly has roots in the autonomous vehicle space. However, most trucking companies have a substantial investment in existing class 8 semi-trailer trucks that are not going to be replaced overnight. Trimble Transportation Mobility is using deep learning technologies, in conjunction with other advanced analytic techniques and state of the art DevOps approaches to help ensure the safe operation of trucking fleets. While it may be premature for many trucking fleets to embrace autonomous vehicles, TTM has made it possible for those same companies to leverage deep learning as a way to reduce costs and improve safety.

When is the Next Recession?: Forecasting with Recurrent Neural Networks

There have been ten US recessions since 1950. When is the next one? The answer matters because asset prices plunge in recessions, which creates both risk and opportunity. Forecasters answer this question by looking at leading economic indicators. We translate the thinking of forecasters into machine learning solutions. This talk explains the use of recurrent neural networks, which excel at learning historical patterns that don’t repeat, but rhyme. Our model anticipates the Great Recession from past data and exhibits lower error than established benchmarks. The proposed approach is broadly applicable to other prediction problems such as revenue and P&L forecasting.