Deep Learning Design Patterns Study Jams - Overview

GDG Seattle
Thu, May 7, 2020, 7:00 PM (PDT)

33 RSVP'ed

About this event

Join us for a multi-session learning series of computer vision with deep learning design patterns!

This 5/7 session is an overview of the learning series: go over what materials are available and how we will study together.

Andrew Ferlitsch from Google Cloud AI team is writing a book called "Deep Learning Design Patterns". He will be providing free video presentations and Colab exercises to help us study deep learning with TensorFlow 2.x (tf.Keras).

Margaret Maynard-Reid is going to be a co-instructor of these learning series.

Instructors bio
Andrew Ferlitsch is an expert on computer vision and deep learning at Google Cloud AI Developer Relations, and formerly a principal research scientist for 20 years at Sharp Corporation of Japan, where he has 115 issued US patents and worked on emerging technologies: telepresence, augmented reality, digital signage, and autonomous vehicles. Currently in his present role, he reaches out to developer communities, corporations and universities, teaching Deep Learning and evangelizing Google's AI technologies.

Margaret Maynard-Reid is a Google Developer Expert (GDE) for Machine Learning. She is a contributor to the open-source ML framework TensorFlow. She writes blog posts and speaks at conferences about on-device ML, computer vision and TensorFlow. She is passionate about community building and helping others get started with AI/ML. She leads "GDG Seattle" and "Seattle Data/Analytics/ML".

Deep Learning Design Patterns
This is Andy's book that overs beginner, intermediate and advanced-level materials on deep learning design patterns.

- It's practical hands-on workshop style besides reading / videos.
- Materials are catered towards job roles instead of academia.

From these Study Jam series you will learn:
- the fundamentals of computer vision and deep learning (beginner)
- deep learning design patterns (beginner)
- how to implement research papers (intermediate)
- AutoML under the hood (intermediate)
- large-scale model architecture & training (advanced)