Introduction to Paul A. Nussbaum, PhD:


Paul Nussbaum has always enjoyed making complex topics easy for everyone to understand.

This ability has allowed him to increase profits for internationals such as Cisco and Ericsson, entrepreneurial startups such as Spectrum Communications and Electronics, AMC Technology, and even individuals forging their own path.

No consultation is too large or too small. Whether or not your project needs university student involvement, Dr. Nussbaum will be there to guarantee on time and on target delivery.
  • Making your products more profitable
  • Making your channels more productive
  • Making your team more effective
  • Making your technology easier for everyone to understand

Reach out today! https://www.linkedin.com/in/pnussbaum/

Comments

Post a Comment

Popular posts from this blog

Image
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...
Read more
Image
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...
Read more
Image
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...
Read more