A Deeper Stack for Deep Learning: Adding Visualizations and Data Abstractions to Your Workflow

Abstract: In this training session I introduce a new layer of Python software, called ConX, which sits on top of Keras, which sits on a backend (like TensorFlow.) Do we really need a deeper stack of software for deep learning? Backends, like TensorFlow, can be thought of as "assembly language" for deep learning. Keras helps, but is more like "C++" for deep learning. ConX is designed to be "Python" for deep learning. So, yes, this layer is needed.

ConX is a carefully designed library that includes tools for network, weight, and activation visualizations; data and network abstractions; and an intuitive interactive and programming interface. Especially developed for the Jupyter notebook, ConX enhances the workflow of designing and training artificial neural networks by providing interactive visual feedback early in the process, and reducing cognitive load in developing complex networks.

This session will start small and move to advanced recurrent networks for images, text, and other data. Participants are encouraged to have samples of their own data so that they can explore a real and meaningful project.

A basic understanding of Python and a laptop is all that is required. Many example deep learning models will be provided in the form of Jupyter notebooks.

Documentation: https://conx.readthedocs.io/en/latest/

Bio: Douglas Blank is a professor of Computer Science at Bryn Mawr College outside of Philadelphia, PA. He has been working with neural networks for over 20 years, and developing easy to use software for even longer. He is one of the core developers of ConX.