What we're about

Welcome to the group. We’re excited to bring you the latest happenings in AI, Machine Learning, Deep Learning, Data Science and Big Data.

Our goal is to congregate with data enthusiasts from all over the Bay Area, exclusively online and discuss trending topics in the world of AI. We also regularly invite esteemed industry influencers and thought leaders who talk shop on all things data science.

We are a professional organization for AI practitioners in Silicon Valley. We aim to bring together data scientists, engineers, and business people working in AI and the big data area. We host seminars, interactive group meetings, and mentoring sessions. We provide an exchange platform for big data professionals to share their experiences, learn about the newest technologies and explore potential startup opportunities. Join us today. Find like-minded people in AI and grow your career and AI and big data business with us.

Sound like a good time? Then come visit our HQ in Santa Clara and get the chance to contact with our community.

If you’d like to speak at future meetups, co-promote your meetup or inquire about sponsorship opportunities, please reach out to our organizers

Thank you for advancing the future of data science,

Evelyn

Upcoming events (4+)

Autotuning Deep Learning Models

Online event

Please register using the zoom link to get a reminder:

https://us02web.zoom.us/webinar/register/WN_apZlfeGbQ7C8Fx1TDsb_4g

The quality of any machine learning or deep learning model depends on the values that define the model structure and corresponding hyperparameters. Many practitioners may find themselves investing countless hours manually searching for the right model and related hyperparameter values. Some use highly inefficient grid search methods. Others will use simple random sampling, which actually works fairly well. But alone, this method only offers a globalized search, and other sampling methods may be better suited to the job.

Why not use machine learning to automate the search for the best model?

This presentation details an advanced approach that uses both global and local search strategies that can be evaluated in parallel to ensure a quick and efficient exploration of the decision space. In the case of this presentation, a genetic algorithm (GA) will be examined for the global search because the selection and crossover aspects of a GA distinguish it from a purely random search. A generating set search (GSS) will be used to greedily search the local decision space.

Agenda:

11:45 am - 11:55 am Arrival, socializing and Opening
11:55 am - 1:00 pm Robert Blanchard, "Autotuning Deep Learning Models"
1:00 pm - 1:10 pm Q&A

About Robert Blanchard

Robert is a Principal Data Scientist at SAS where he builds end-to-end artificial intelligence applications. He also researches, consults, and teaches machine learning with an emphasis on deep learning and computer vision for SAS. Robert has authored a book on computer vision and has developed several professional training courses on topics including neural networks, deep learning, and optimization modeling. Before joining SAS, Robert worked under the Senior Vice Provost at North Carolina State University, where he built models pertaining to student success, faculty development, and resource management. Robert also started a private analytics company while working at North Carolina State University that focused on predicting future home sales. Prior to working in academia, Robert was a member of the research and development group on the Workforce Optimization team at Travelers Insurance. His models at Travelers focused on forecasting and optimizing resources. Robert graduated with a master’s degree in Business Analytics and Project Management from the University of Connecticut and a master’s degree in Applied and Resource Economics from East Carolina University.

Please register using the zoom link to get a reminder:

https://us02web.zoom.us/webinar/register/WN_apZlfeGbQ7C8Fx1TDsb_4g

Webinar ID:[masked]

Modern Approaches To Anomaly Detection

Online event

Please register using the zoom link to get a reminder:

https://us02web.zoom.us/webinar/register/WN_9tINfoQwQD-7h-gLSmkvNQ

Identifying anomalous observations has important business impacts across all industries. None more than in the world of fraud detection where some observations are intentionally trying to hide, which is different than most rare event problems that exist in modeling. This talk will highlight some modern approaches to anomaly detection – local outlier factors, isolation forests, and classifier adjusted density estimation (CADE). All of these techniques have foundations in places that were not originally anomaly detection. Local outlier factors are derived from k-nearest neighbors. Isolation forests have their foundation in tree based algorithms. CADE was originally designed as an improvement / variation on kernel density estimation. However, all of these have been shown to have great abilities to find anomalous observations in a data set.

Agenda:

11:45 am - 11:55 am Arrival, socializing and Opening
11:55 am - 1:00 pm Aric LaBarr, "Modern Approaches To Anomaly Detection"
1:00 pm - 1:10 pm Q&A

About Aric LaBarr

An Associate Professor of Analytics at the Institute for Advanced Analytics, Dr. Aric LaBarr is passionate about helping people solve challenges using their data. There he helps design the innovative program to prepare a modern work force to wisely communicate and handle a data-driven future at the nation's first Master of Science in analytics degree program. He teaches courses in predictive modeling, forecasting, simulation, financial analytics, and risk management. Previously, he was Director and Senior Scientist at Elder Research, where he mentored and lead a team of data scientists and software engineers. As director of the Raleigh, NC office he worked closely with clients and partners to solve problems in the fields of banking, consumer product goods, healthcare, and government. Dr. LaBarr holds a B.S. in economics, as well as a B.S., M.S., and Ph.D. in statistics — all from NC State University.

https://us02web.zoom.us/webinar/register/WN_9tINfoQwQD-7h-gLSmkvNQ

Webinar ID:[masked]

Generating Synthetic Data With GANs

Online event

Please register using the zoom link to get a reminder:

https://us02web.zoom.us/webinar/register/WN_-6YyWkajRNy49byw2oumpg

Do we have enough data? Are our datasets imbalanced? How can we accelerate research while avoiding data leakage? Generative Adversarial Networks (GANs) are a promising AI solution to these questions. With GANs, we can generate more and better data that is more fair and generalizable, which can be used to improve ML models and algorithm testing. GANs are also promising in the field of data privacy, since they could break barriers to data sharing, allowing companies and institutions to accelerate research findings.

Agenda:

11:45 am - 11:55 am Arrival, socializing and Opening
11:55 am - 1:00 pm Marta Batlle López, "Generating Synthetic Data With GANs"
1:00 pm - 1:10 pm Q&A

About Marta Batlle López

As a data scientist in Pharma Informatics and Product Development at Roche, Marta is currently leading the development of a Deep Learning group focused on Generative Adversarial Networks and their applications in healthcare. In the past, she has also worked on different projects in the field of Natural Language Processing to speed up clinical trials. She is currently a One Young World Ambassador for Roche, with the goal to promote AI fairness within the company. Prior to joining Roche, she completed an MSc in Health Data Science and an MRes in Neuroscience at University College London.

Please register using the zoom link to get a reminder:

https://us02web.zoom.us/webinar/register/WN_-6YyWkajRNy49byw2oumpg

Webinar ID:[masked]

Deep Embeddings and Section Fusion for Music Segmentation

Please register using the zoom link to get a reminder:

https://us02web.zoom.us/webinar/register/WN_DfwFTpQuTp2JizrqO1Co1Q

Music segmentation algorithms identify the structure of a music recording by automatically dividing it into sections and determining which sections repeat and when.

In this talk I give an overview of this music information retrieval problem and present a novel music segmentation method that leverages deep audio embeddings learned via other tasks.

This approach builds on an existing segmentation algorithm replacing manually engineered features with deep embeddings learned through audio classification problems where data are abundant. Additionally, I present a novel section fusion algorithm that leverages the segmentation with multiple hierarchical levels to consolidate short segments at each level in a way that is consistent with the segmentations at lower levels.

Through a series of experiments and audio examples I show that this method yields state-of-the-art results in most metrics and most popular publicly available datasets.

Agenda:

11:45 am - 11:55 am Arrival, socializing and Opening
11:55 am - 1:00 pm Oriol Nieto, "Deep Embeddings and Section Fusion for Music Segmentation"
1:00 pm - 1:10 pm Q&A

About Oriol Nieto:

Oriol Nieto (he/him or they/them) is a Senior Audio Research Engineer at Adobe Research in San Francisco. He previously was a Staff Scientist in the Radio and Music Informatics team at Pandora, and holds a PhD from the Music and Audio Research Laboratory of New York University. His research focuses on topics such as music information retrieval, large scale recommendation systems, music generation, and machine learning on audio with especial emphasis on deep architectures. His PhD thesis is about trying to better teach computers at “understanding” the structure of music. Oriol develops open source Python packages, plays guitar, violin, cajón, and sings (and screams) in their spare time.

Please register using the zoom link to get a reminder:

https://us02web.zoom.us/webinar/register/WN_DfwFTpQuTp2JizrqO1Co1Q

Webinar ID:[masked]

Past events (194)

Data Science Resume And Interview Tips

Online event

Photos (650)