This is a hands-on introduction to deep learning using Tensorflow.js, Google’s open-source JavaScript framework for machine learning.
The workshop is project-based, leading participants through the process of building a machine learning-based image recognition application from scratch.
Course Description
Participants will master a critical subset of the Tensorflowj.s API: tensor manipulation, the graph, stacking network layers, training, and model export. Knowing how to put those pieces together at a practical API level empowers the participant to start solving problems and experimenting with what works.
The workshop will build intuition and understanding, through examples and applications, of the foundational ML concepts necessary for effectiveness: optimization, convolutional neural networks, loss functions, and validation. You will also learn essential tips and tricks to get optimal model performance: hyper-parameter tuning, data augmentation, architecture design, and filter visualization.
There will be ample time for practicing fundamentals, asking questions, and exploring beyond the presented material. We are here to help you interalize this material, make it your own, and have fun while doing it!
Why Tensorflow?
While there are many deep learning frameworks out there, Tensorflow’s combination of performance, ease-of-use, and a rich developer ecosystem makes it an excellent choice for wide range of research and production use cases. Tensorflow has the best integrated deployment story of any framework, can be used with many different platforms and languages, and offers best-in-class utilities for visualization, debugging, and monitoring.
Course Outline
Participants will learn to:
- Program with the Tensorflow.js API
- Create and train image recognition models
- Diagnose, evaluate, and visualize models
- Tune a model and tweak your model architecture
- Integrate your trained models
Who is this program for?
This workshop is for anyone who is interested in building deep learning systems with previous programming experience. Whether you are a software engineer interested in using deep learning at your job, a creator using deep learning on a new project, or an engineering manager interested in defining your team’s AI strategy, you will come away with newfound skills and knowledge to apply to your domain of choice.
You should also have some experience with programming in JavaScript and understand basic linear algebra. If you need a refresher on either of these requirements before the workshop, we recommend you take a look at this this tutorial.
Details
Schedule
9:00 AM – 5:00 PM on both days. Doors open at 8:45 AM.
Cost
Corporate Early Bird:
$1,250 per person
Indie Early Bird:
$800 per person
Cost is full admission to the two-day program. Breakfast and lunch are provided on both days.
If you would like to attend but are unable to because of the cost, we offer a limited number of need-based scholarships. Send an email to hello@kitchentablecoders.com with a few sentences on your background and why you would like to attend this class. Priority will go to underrepresented groups, but we will consider everyone on a case-by-case basis.
If you or your company is interested in sponsoring the event please get in touch hello@kitchentablecoders.com.
Event Location
274 Morgan Ave, 4th floor
Brooklyn, NY 11238
Subway: L Grand Stop
What should I bring?
Bring a laptop!
Instructors
Evan Casey
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.
David Nolen
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.