About The Author

I have over 20 years of experience in product management at telecommunications companies such as Ericsson and Cisco, as well as small start-up companies. I am also a university professor, having been with East Coast Polytechnic Institute (ECPI) as an instructor and in various director roles (program, academic affairs, and faculty development).

My PhD research in biomedical signal analysis, built upon my prior work with pattern analysis, deep learning, and artificial intelligence, where I am ranked as an "Expert" on the Google AI site Kaggle.com (which I highly recommend).

My greatest pleasure comes from explaining the complex in simple ways everyone can understand - and seeing people use that knowledge to go on to do great things.

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Reverse Inference Example Software:  This points to example software on GitHub where you can see some of these concepts in action. Here is the link: https://github.com/prof-nussbaum/Reverse_Inference  Reverse_Inference Working backwards through a deep convolutional network, to recreate the input image - and identify areas for improvement. Please see this article for more details on this technique, that I call "Reading the Robot Mind." https://readingtherobotmind.blogspot.com/2021/03/reading-robot-mind-deep-convolution.html What if we could read the robot's mind? It sounds like a silly statement, but if you could read a person's mind, you could "see" what they were thinking of when they mention a classification. If they say "I am seeing a dog" - reading their mind would give you additional details about the dog, or even perhaps, see what they are seeing. This is the same premise for Artificial Intelligence and Machine Learning. I know that deep conv...
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Auto-Associative How To - INCLUDING SOFTWARE CODE This repository includes "how to" examples of auto-associative neural networks. These networks have the ability to encode and decode (CODEC) sample data in the most efficient and low-loss way. The minimization of the loss function is accomplished through the use of gradient descent training algorithms. Example 1 - Auto-Associative How to (using TGS Salt Columns) The first "how-to" example comes from a competition held on the kaggle.com website. WHAT IS THE OVERARCHING GOAL? The competition, as well as details about the data set used for training, can be found here: https://www.kaggle.com/c/tgs-salt-identification-challenge . The purpose of the competition is to see who best can create an AI system that classifies seismic data. There are human experts who can do this classification from the original data, and indeed have done so to create the training and testing data. WHY USE AUTO-ASSOCIATIVE NETWORKS? In many case...
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Reading the Robot Mind: Deep Convolution Networks Decoded Even as "Explainable AI" is all the rage, coding/transformation fidelity is a critical success factor. Whether you are using frequency bands from Fourier transforms, statistical features of Wavelet decomposition, or various filters in Convolution Networks - researchers must be able to perform the reverse coding/transformation to see if they have retained sufficient information for classification. Without this, they are only guessing via network architecture trial and error. These tools are sorely lacking in Keras on Tensorflow in Python, so I wrote my own. I would like to see these more generalized and made into public libraries. Question: Who can point me to current work in this area, or can give advice on next steps in my effort? EXAMPLE: In a deep convolution network trained to recognize Bengali handwriting, this diagram examines the 64 filters after six layers of 2 dimensional convolution. It performs the reverse t...
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