AI Atelier is a workshop series in New York City focused on enabling people of all backgrounds to understand and build deep learning systems.
Deep learning is dramatically improving medicine, art, agriculture, finance and many other fields. We believe these methods will become an essential skill for the software engineer and creator as we further unlock its vast potential.
At AI Atelier you will develop both practical foundations and hands on engineering experience. While you need to know how to program to attend AI Atelier, we’ve had students attend with as little as six months of programming experience and as much as 30 years of professional programming experience.
Our classes are crafted to give you a framework for applying deep learning to real world problems. While there are many online resources that focus solely on the theoretical underpinnings of AI, there are few that provide practical guidance on how to design, tune, debug, and build deep learning systems from the ground up.
We believe computational thinking is a fundamentally creative endeavor and the series emphasizes intuition and innovation as much a technical capability. Every workshop is project-based, and you will come away with a fully-functioning machine learning project.
David Nolen is a software engineer with a background in programming languages, compilers, databases, and expert systems. He has taught at New York University, been a resident at of the Recurse Center and worked for Princeton University, The Modern Museum of Art, and The New York Times. He is an international speaker on the usage of functional programming techniques to simplify software development practice. He is currently employed at Cognitect building systems for clients around the world. With Amit Pitaru he founded the Kitchen Table Coders workshop series in Brooklyn. He likes to play Go. David is active on Twitter @swannodette.
Kovas Boguta has spent 15 years in scientific computing, data infrastructure, and machine learning roles. At Wolfram Research he helped build Mathematica and Wolfram Alpha, and served as instructor at the Wolfram Science summer school. At Weebly, Kovas built out the Hadoop-based analytics infrastructure. At Twitter, Kovas helped develop the internal deep learning platform, and is now applying ML to recommendations problems. His favorite cellular automaton is Rule 110. Kovas is active on Twitter @kovasb.
Evan Casey is an engineer and researcher interested in generative systems and reinforcement learning. He currently works on autonomous skill learning for robotics at Cogitai. In the past he has worked on real-time ad bidding engines, recommendation systems, and large-scale data infrastructure. He is an alumnus of the Recurse Center and HackNY and is an avid skateboarder and surfer. Evan is active on twitter @ev_ancasey.
Amit is a coder and designer. He is currently at Google Creative Lab exploring the intersection of AI techniques and creative expression. Formerly a professional musician, his work to date spans across experimental art, research, education, and entrepreneurial projects. A recurring theme in Amit’s work is the extreme attention to user-experience, specifically how technology can promote (or destroy) curiosity, literacy, and creativity.
As an educator, Amit develops curricula that focus on the coupling of technology and the creative thought process. He taught at New York University’s ITP and Cooper Union’s Arts department. Amit is also the co-founder of Kitchen Table Coders. Amit is active on Twitter @pitaru.