Join IBM Data Scientist and Developer Advocates Jeremy Nilmeier and Developer Advocate David Nugent for an interactive workshop on TensorFlow and IBM Watson.

RNNs and LSTMs have enjoyed great success in text generation algorithms, but their use in other fields has not been as widely studied. We will discuss our experiences and progress using Recurrent Neural Networks to make predictions on arbitrary multivariate time series data. Our first study used weather data from the JFK terminal over several years. We will discuss the issues related to tuning and validating this model, as well as how we migrated this model into the Model Asset Exchange, which is an IBM hosted API for making predictions on data using pre-trained neural network models. Our insight into tuning this model allowed us to provide another API via Watson Machine Learning, which is a hosted service that allows user defined data and models to be uploaded, trained, and tuned on GPU accelerated Watson Studio Notebooks on demand hardware using simple remote API calls. We will discuss examples from the financial sector, weather prediction, and other important time series prediction use cases.

Machine learning developers or those interested in machine learning and TensorFlow.

Jeremy Nilmier, IBM OS Developer & Developer Advocate

Dave Nugent, IBM Developer Advocate in San Francisco https://twitter.com/drnugent

Machine Learning Time Series Prediction with TensorFlow