This tutorial is a gentle hands-on introduction to developing predictive models using deep learning artificial neural networks. It provides a high-level overview of the key elements of neural networks and deep learning (BP, CNN, LSTM), along with recent advances that allow deep networks to solve challenging problems such as object recognition in images (e.g. classification of animal or letter) and sequence prediction (e.g. next word in a sentence, like Google auto-complete).
Participants build their own deep models using prepared software (Keras and Tensorflow) working in the browser. All necessary code is provided, however a basic level of Python programming experience is needed.
Requirements:
A laptop or desktop with the latest version of the Chrome browser, and a Google account for using Google Drive.
Intended Audience:
Students, faculty, researchers, business professionals or anyone seeking an introduction to deep learning technology.
This workshop is delivered by the Acadia Institute for Data Analytics, hosted by ACENET.
