By Dr. Charles H. Martin, University of Chicago in Theoretical Chemical Physics, NSF Fellow, CalculationConsulting.com
SCHEDULE: (times in PT, in the San Francisco bay area)
6:50 connect to streaming
7:00 Event and speaker introduction
7:10 Speaker begins
WeightWatcher (WW): is an open-source, diagnostic tool for analyzing Deep Neural Networks (DNN), without needing access to training or even test data. It can be used to:
* analyze pre/trained PyTorch, Keras, DNN models (Conv2D and Dense layers)
* monitor models, and the model layers, to see if they are over-trained or over-parameterized
* predict test accuracies across different models, with or without training data
* detect potential problems when compressing or fine-tuning pre-trained models
* layer warning labels: over-trained; under-trained
as well as several new experimental model transformations, including:
* SVDSmoothing: builds a model that can be used to predict test accuracies, but only with the training data.
* SVDSharpness: removes Correlation Traps, which arise from sub-optimal regularization pre-trained models.
I do machine learning, deep learning, data science, and AI software development, and with extensive domain experience in NLP for Search Relevance (as well as Text Generation and Quantitative Finance).
I have personally developed machine learning (ML) systems and helped get them into production at companies including Roche, France Telecom, GoDaddy, Aardvark (Google), eBay, eHow, Walmart, Barclays/BGI, and Blackrock. Recently I was both a consultant and FTE distinguished engineer at GLG, a very prestigious international consulting firm, where I developed AI methods for the search and recommendations platform.
I provide scientific consulting to the Page family office at the Anthropocene Institute, advising on areas of nuclear and quantum technologies with an eye toward climate change.
I do scientific research in collaboration with UC Berkeley on the foundations of AI and am the lead on the WeightWatcher project: pip install weightwatcher.
In 2011, I helped Demand Media / eHow become the first $1B IPO since Google: http://www.youtube.com/watch?v=6Cv_Rv56TA0
I was at Aardvark, acquired by Google and featured in the Lean StartUp, and a subject matter expert (SME) in ML at eBay.
At my first startup, in the late 90s, I developed semi-supervised ML algoRITHM for personalized search.
I offer over 15 years of commercial data science, software engineering, and ML experience. I am a full stack developer for web, object-oriented, and numerical programming. This includes java, ruby, python--and even dev ops.
I was coding on the Cray XMP in High School. I invented a Monte Carlo method for non-eq condensed matter systems and published--at 19. I am a national math contest winner.
My PhD is in Theoretical Chemistry from U Chicago. I was an NSF Fellow (1 of 2 nationwide).