What we're about

We’re excited to bring you the latest and practical technology on AI, Machine Learning, Deep Learning, Data Science and Big Data.

Our goal is to congregate with AI enthusiasts from all over Chicago to learn and practice AI tech, through tech talks, study jams, code labs etc.. we regularly invite tech leads from innovated companies, successful startups to share their practice experiences and practices in the world of AI, Cloud, Data, Blockchain.

If you’d like to speak at future meetups, co-promote your meetup or inquire about partnership opportunities, please feel free to reach out to us.

Thank you

Kelvin, Community Manager

===Tentative Agenda =====

5:30pm - 5:50pm, Snack & socia

5:50pm - 6:00pm, Intro/announcemen

6:00pm - 7:00pm, Tech Talk 1 and Q&A

7:00pm - 8:00pm, Tech Talk 2 and Q&A

8:00pm - 8:30pm, Lucky draw & Mingle

**Learn applied AI tech online with 80000+ developers globally, via webinars, live online courses, bootcamps: https://learn.xnextcon.com

** AI Developer Conference (Seattle, San Francisco, New York, Beijing) : http://www.xnextcon.com

Upcoming events (3)

4-Weeks AI course: Deep Learning for Computer Vision (Cohort 6)

Paid online live course (using zoom), follow instructions below to enroll ----------- 4-week AI course: Deep Learning for Computer Vision (Cohort 6) Start date: 12/1~12/22, 5:00pm~7:00pm PST, Every Tue&Thu. Enrollment: https://learn.xnextcon.com/course/coursedetails/C2020120117 ** 40% off promotion price ends soon. This course is online live course using zoom and slack group support. We meet twice a week at online classroom (powered by zoom). you can listen, watch, interact, Q&A with instructors, hang out with instructors/peer students pre/post sessions. If you miss the live session, you can learn session replay with recordings, course materials any time. The course include: * 4 weeks / 8 sessions / 16 hours * Code labs and Capstone project * Live Sessions, Real time interaction * Slack support after class and homeworks Details: This 4 weeks immersive instructor-led course will teach everything you need to know to become a software engineer in computer vision and deep learning! You will learn: * Recognize problems can be solved with deep learning * Select the right technique for the problems. * Master deep learning algorithms, models and computer vision tech * Master the most popular tools like numpy, Keras, Tensorflow, and openCV * Master google cloud deep learning pipelines This course is packed with practical exercises and code labs. not only will you learn theory, but also get hands-on practice building your own models, tuning models, and serving models * Practical walkthroughs that present solutions to actual, real-world image classification problems and challenges * Hands-on tutorials (with lots of code) that not only show you the algorithms behind deep learning for computer vision but their implementations as well * A no-nonsense teaching style that is guaranteed to cut through all the cruft and help you master deep learning for image understanding and visual recognition * End to end deep learning pipeline from building models to deploy and serve models

AI webinar series #4: Practical Guide to Hyperparameter Optimization

Free online AI tech talk event, you can join from anywhere with zoom, Register and attend: https://learn.xnextcon.com/event/eventdetails/W2020120210 Abstract: Long before machine learning ventured outside of academia, hyperparameters had a different name. “Magic Numbers”. These were unexplained constants appearing in code, and their values completely (and destructively) controlled the behavior of programs since the 1960s. One day, someone (probably a physicist) realized that some of their hardcoded constants could be altered, thereby generating completely new behavior in the software they were working on - without changing anything else. The constants were now parameters as well, and the hyperparameter was invented. Fast forward more than half a century into the future, these “list of constants” are no longer tweaked by the shaky hands of weary grad-students, but are methodically swept to find “performance-enhancing” configurations. This is done by very fancy algorithms that are designed to find these “lucky” combinations using the least resources possible. Such sweeping mechanisms are fairly easy to mismanage and may result in a huge waste of money, time, and energy. Nevertheless, every now and then comes a rare combination of an efficient optimization mechanism for the search - and a robust and easy way to perform it. In this webinar, we will gawk at the amazing wealth of this realm and use open- source tools to move from a simple grid search - to algorithms verging on Bistromathics, all by keeping a neat interface and controllable execution. Social networking with speakers, attendees 30mins before/after the event on slack. Join slack by the invitation: https://bit.ly/3gi7bjf . The two channels: #jobs for job posting from speakers, partners, sponsors companies, and you can Q&A with hiring managers right in the channel. #events for events Q&A, mixing and networking with speakers and other peer attendees.

Machine Learning Rapid Prototyping with Watson AutoAI

Online event

Free online AI tech talk event, you can join from anywhere with zoom, Register and attend: https://learn.xnextcon.com/event/eventdetails/W2020120310 Agenda: * pre-event networking (20mins) * community updates, jobs/interns/talents announcements. (5mins) * tech talk (45mins) * Q&A and closing (20mins) Social networking with speakers, global attendees 30mins before/after the event with community update, AI jobs/intern openings, talents available announcements, etc.. Join slack by the invitation: https://bit.ly/3gi7bjf Abstract: Welcome to the "AI Trust, Bias and Explainability" learning series, by IBM AI. In collaboration with IBM team, we host a series of practical introductory sessions to AI trust, bias and explainability. This is the 8th session: An emerging trend in AI is the availability of automation technologies that train several models and select the one with best-fit. This automated AI process includes several variations of feature engineering and hyperparameter optimization that aim to improve the model. In this lab, you will use the Watson Studio AutoAI tool to build a rapid prototype and generate a Python notebook for your prototype. You will then examine each of the steps in the Python notebook to see how the AutoAI performed. Speakers: Austin Eovito Data Scientist in IBM, who focuses on the balance of bleeding-edge research produced by academia and the tools used in applied data science. William Roberts Data Science Evangelist at IBM. Will writes for the IBM Data Science community, and creates technical content for other data science practitioners. He is also a host on the IBM Developer Data Scientist Podcast series, and co-editor for the IBM Community newsletter. Before joining "Big Blue" to build a community around the latest in Artificial Intelligence, he was a consultant with Red Hat specializing in middleware deployments for financial clientele

Past events (117)

Photos (67)