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Deep Learning Series Part 2: The Devil’s in the Details
June 6, 2017 @ 6:30 pm - 7:30 pm
Metis is hosting the second part the on-going Deep Learning Series!
Learning rates. Momentum. Adding hidden layers. We’ve seen these terms thrown around when learning about neural nets. But what do they really mean? How can we make these notions precise and code them up? In this workshop, we’ll extend the example of coding a neural net from scratch that we started going through in the first Meetup of this year. We’ll also learn a bunch of techniques to maximize the effectiveness of our neural net, learning which tricks matter and which don’t. Attendees will leave here feeling more confident knowing “what is going on” with the tricks used to tune neural nets.
Seth Weidman, Senior Data Scientist at Metis
Seth has done quantitative consulting and data science for 5 years. He is currently a Senior Data Scientist at Metis but has worked in the consulting industry, internally for Capital One, and at Trunk Club. A former aspiring mathematician, he endeavors to understand the math behind neural nets and back propagation. sethweidman.com
This is the second part in Jeremy Watt’s Deep Learning series. Jeremy has also taught at Metis and is the author of Machine Learning Refined.
Metis (thisismetis.com) accelerates careers in data science by providing full-time immersive bootcamps, evening part-time professional development courses, online resources, and corporate programs based in Seattle, New York, Chicago, and San Francisco.
Brought to you by Kaplan, Metis focuses primarily on Python, machine learning, data visualization, deep learning, big data processing, statistical foundations, and more. Students and alumni of the bootcamp program receive continuous support from our career advisors, empowering them to pursue a successful career in the fast-growing field of data science.
Learn more about thisismetis.com.