Machine vision using deep-learning -- Introduction and basics
jeu. 28 février à 22:00
• What we'll do:
This is a hands-on workshop on machine vision. During this workshop, attendees will learn: 1) What is a convolutional neural network by building one, 2) Transfer learning, 3) Object prediction and building object prediction pipelines. We will build a neural network from scratch and train it to recognize images. Once we create a network, we will improve this model using a strategy called transfer learning. Once we understand the concepts of transfer learning, we will train a deeper network called Inception version 3 to create a state of the art image classifier. The projects will use OpenCV, Tensorflow and Keras; three very popular machine vision and deep-learning tools. We will also cover the basics of deploying scalable python applications in the cloud. The course is hosted using either our own cloud platform: Jomiraki, a cloud connected AI developer environment or Google CoLab, a GPU powered Jupyter compatible deep-learning instance. Either of these environments will be set-up ahead of time, with zero end-user dependencies. This will ensure that each participant will spent more time testing and running the code, instead of trying to figure out the set-up process itself. • What to bring:
This a bring your own device (BYOD) event. For optimal experience, Moad machine vision team recommends Chrome >=71, to access the course contents. • Pre-requisites:
Please set-up a Kaggle and GitHub account ahead of time. Both accounts are needed to get the full benefit of the code examples. • Important to know:
This is a paid event. The tickets are available here: https://www.moad.computer/store/p36/Healthcare_Analytics.html
Use the coupon code: Meetup_[masked]
The coupon code will give 50% discount. Only 10 coupons available. Coupons expire 24 hours before the event.
This is an introductory workshop on machine vision. This course is part of the FutureReady boot-camp by Moad Computer. If you are interested in participating in the boot-camp, please fill-out this form: https://goo.gl/forms/TzClAtTqOLwHcudv1
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