Capable of accepting and mapping complex relationships hidden within structured and unstructured data, Neural Networks are a subset of Deep Learning that involves layers of neurons interacting with, transforming, and passing data through successive layers to develop highly flexible and robust predictive models.

Whether they’re used for regression, classification, feature representation, forecasting, and/or data generation, Neural Networks are adept at untangling complex, real-world problems. Useful for modeling both structured and unstructured data types, Neural Networks have contributed to recent breakthroughs in industries spanning across finance, healthcare, the life sciences, climatology, video remastering, natural language processing, and business analytics for decision-making, modeling, and generative purposes.

Focusing on the Keras API with TensorFlow, this Hands-on-Workshop equips attendees with the skills and knowledge necessary to understand Deep Learning fundamentals in Python. By developing, training, and evaluating their own Artificial and Convolutional Neural Networks, attendees will better understand the potential applications, limitations, and implications of adopting Deep Learning methodologies into their organizations' existing (or new) AI workflows.