What we're about

This Meetup group supports the SF Bay ACM Chapter. You can join the actual SF Bay Chapter by coming to a meeting - most meetings are free, and our membership is only $20/year !

The chapter has both educational and scientific purposes:
- the science, design, development, construction, languages, management and applications of modern computing.
- communication between persons interested in computing.
- cooperation with other professional groups

Our official bylaws will be available soon at the About Us page (http://www.sfbayacm.org/about-us/) on our web site. See below for out Code of Conduct.

Videos of past meetings can be found at http://www.youtube.com/user/sfbayacm

Official web site of SF Bay ACM:

http://www.sfbayacm.org/

Click here to Join or Renew (http://www.sfbayacm.org/join-us/)

Article IX: Code of Conduct - from the ACM Professional Chapter Code of Conduct

Harassment or hostile behavior is unwelcome, including speech that intimidates,creates discomfort, or interferes with a person’s participation or opportunity for participation, in a Chapter meeting or Chapter event.Harassment in any form, including but not limited to harassment based on alienage or citizenship, age, color, creed, disability, marital status, military status, national origin, pregnancy, childbirth- and pregnancy-related medical conditions, race, religion, sex, gender,veteran status, sexual orientation or any other status protected by laws in which the Chapter meeting or Chapter event is being held, will not be tolerated. Harassment includes the use of abusive or degrading language, intimidation, stalking, harassing photography or recording,inappropriate physical contact, sexual imagery and unwelcome sexualattention. A response that the participant was “just joking,” or “teasing,”or being “playful,” will not be accepted.2. Anyone witnessing or subject to unacceptable behavior should notify a chapter officer or ACM Headquarters.3. Individuals violating these standards may be sanctioned or excluded from further participation at the discretion of the Chapter officers or responsible committee members.

Upcoming events (5)

Machine Learning and Deep Learning Boot Camp - July 14 to September 10

*** NO RSVP on MEETUP *** Registration NEEDED at: https://valleyml.thinkific.com/bundles/machine-learning-and-deep-learning-boot-camp?coupon=acmsuperearlybird ACM discount codes: acmsuperearlybird for 40% ends by June 2nd https://valleyml.thinkific.com/bundles/machine-learning-and-deep-learning-boot-camp?coupon=acmearlybird ACM Discount acmearlybird for 25% ends by July 1st Attendees can get IEEE PDH Certificate from IEEE Continuing Education. ValleyML Machine Learning and Deep Learning Boot Camp -2020 Build a solid foundation of Machine Learning / Deep Learning principles and apply the techniques to real-world problems. Get IEEE PDH Certificate. Virtual Live Boot Camp from July 14th-Sept 10th. This is a Machine Learning and Deep Learning Boot Camp, which is available to register as a complete course of 8 workshops and also as a custom package of individual workshops. Entire Program schedule along with instructor bios can be downloaded here as a PDF. https://drive.google.com/file/d/1XyOg9v4m6EwPQSPfS74-BCLgjbw0LFzk/view Short link http://bit.ly/mldlbc2020 The Boot Camp consists of the following 8 workshops which cover fundamentals of machine learning to the latest advances of deep learning technologies and their applications. Live lessons with live Q&A will be held on Tuesday and Thursdays every week from July 14th to September 10th 2020. Each session is from 3 pm to 4.30pm pacific time. Live sessions are planned through Zoom Webinar. The total number of live instruction hours are 27 (3 hours each week for 9 weeks). Topics: Essential Math, Python and Tensorflow for Machine Learning July 14th, July 16th Statistical Methods for Data Science July 21st, July 23rd, July 28th, July 30th Classic Machine Learning Aug 4th, Aug 6th Deep Learning with CNN and Tensorflow Aug 11th, Aug 13th Deep Learning for Computer Vision Aug 18th, Aug 20th Deep Learning for Natural Language Processing Aug 25th, Aug 27th Deep Learning for Robotics Sept 1st, Sept 3rd Deep Learning for Intelligent Video Analytics Sept 8th, Sept 10th Course Logistics: ValleyML.ai is working with Apollo AI, an approved IEEE Education/Course provider, to offer this course. This event is reviewed and approved by IEEE continuing Education. Please check the ValleyML learning platform https://valleyml.thinkific.com/bundles/machine-learning-and-deep-learning-boot-camp?coupon=acmsuperearlybird for more details and FAQ on this offering. Venue: Virtual (Over Zoom) Attendees would have access to course materials (presentations and programming exercises as well as video resources). A first module would be available immediately and the other modules would be available 8 days after enrollment. Help would be available through Thinkific communities, which registered attendees have access and ask questions. The topics covered in each workshop are described in the course webpage. For workshops 5 - 8, a project with solutions is provided. Instructors: Dr. Kiran Gunnam, IEEE Distinguished Speaker and Distinguished Engineer - Machine Learning & Computer Vision at Western Digital Dr. Koji Seto, Chief Scientist, Apollo AI Inc. Dr. Osso Vahabzadeh, Principal Research Scientist, Apollo AI Inc. Dr. Tirthajyoti Sarkar, Senior Principal Technologist, ON Semiconductor Instructors:

Selling Analytics: Getting People to Use Your Models

Online event

By Sean Murphy, 7:00 Announcements, ZOOM hosted by AICamp Please register at here: https://us02web.zoom.us/webinar/register/WN_Csm33MCcR7CZwxeGEKkZcQ Youtube live stream: https://youtu.be/KvrKbdHkoFo Asbtract What's harder than developing models and analytics for complex business problems? Getting people to not only look at them but incorporate them into their decision making processes. Whether you are working inside a larger firm or at a startup looking for new customers for your analytic models, it can be very challenging not only to get reliable information and management attention but also to cultivate enough confidence in the output that it helps to inform decisions. Key Take-Aways • How to ensure you are addressing a critical business issue. • How to diagnose the underlying constraints and true costs a customer (whether internal or external) faces in acting on your model. • How to ensure your model results are based on solid information • How to test if your "answers" are really actionable. Speaker Bio Sean Murphy has worked in a variety of roles in the last twenty-five years: software engineer, engineering manager, project manager, business development, product marketing, and customer support. Companies he has worked directly for include Cisco Systems, 3Com, AMD, MMC Networks, and VLSI Technology. He has a BS in Mathematical Sciences and an MS in Management Science from Stanford.

Knowledge graph extraction from unstructured data and Semantic Role labeling

Streaming will be available the day of the event. SCHEDULE: 6:50 connect into Zoom webinar 7:00 introduce upcoming talks and speaker 7:10 speaker starts 8:20 - 8:45 speaker finishes 9:00 all are out ABSTRACT: Maintaining regulatory compliance is challenging for a range of businesses due to the volume of regulations and the rapid rate of change. Given the sheer volume, it is difficult for an enterprise to maintain a clear picture of the state of regulations that govern them, or to stay abreast of the changes. In contrast to expert analysis or the development of domain-specific ontology and taxonomies, this talk will discuss how a task-based approach for fulfilling specific information needs within a new domain can be helpful. This presentation will discuss various techniques for knowledge graph extraction and completion, domain-specific schema creation, custom bi-LSTM-CRF model for entity extractors and attention-based deep Semantic Role Labeling. We will walk through each of these algorithms in detail and their need for a specific use case. SPEAKER BIO: Vivek Khetan is an artificial intelligence researcher at Accenture Labs, San Francisco. He is currently focusing on semantic role labelling, entity, and relationship extraction, close, and open-domain knowledge graph creation. He has scholarly work published in the ECIR and Information Retrieval Journal. Currently, he is working on “Common Sense Reasoning” and also organizing the Knowledge Graph for Social Good (KGSK) workshop in collaboration with the United Nations. The KGSG workshop will be part of the Knowledge Graph Conference happening at Columbia University. In previous roles, he has worked as a dialogue system researcher at SparkCognition. He has experience in the application of diverse machine learning methods, including information retrieval, survival analysis, and anomaly detection. Vivek has a master's degree from the University of Texas at Austin, with a specialization in Data Mining and Machine Learning and a bachelor's degree from the Indian Institute of Technology (IIT), Dhanbad, India. In his leisure time, Vivek likes to read novels, go for a hike, and explore SF coffee place. See also: https://www.linkedin.com/in/vivekkhetan/

Productivity Implications and testing challenges on Moving to Microservice

By Rajiv Bhatia, MD, MPH, Stanford University The event is jointly organized by ValleyML and SF Bay ACM. 7:00 Announcements, Chapter Election and Presentation Youtube live stream: https://youtu.be/JEpV9T8jM1E Abstract Microservices based architectures are increasingly popular as a way of managing the scalability of large software systems. There are implications for organizational structure, product architecture, development methodology, test strategy, and design and simulation tools. Intended for managers and executives considering the implications of adopting a microservices architecture for a major software system. if you start with microservices as a goal in mind and do not focus on restructuring your organization as well, you will likely end up with a distributed monolith. After you reach a certain complexity of the system under test, the test suite needs to be split into different levels of abstraction to manage the test complexity. Key Take-aways • Factors that determine crossover point for switching to microservices from a software monolith from developer productivity and application scaling perspectives. • Options and trade-offs for testing microservices using real and virtual resources • Planning your migration and getting started. Speaker Bio Jeff Allison has 30 years of experience in the high technology computing and networking industries. He has held various roles in Hardware Engineering, Marketing and Engineering management. He has a proven track record of developing high power cross functional teams to solve complex engineering issues and drive methodology changes throughout the organization. At Cisco he built and managed global engineering development and services teams to deliver next generation routing solutions. He was also responsible for driving and implementing many continuous improvement and product quality initiatives to improve overall customer satisfaction.

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