• Join remotely here: https://meet.google.com/qij-pjqj-acr
• 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.
This week, we will be going step-by-step to deploy the deep Bayesian image classifier notebook in Google CoLab: https://github.com/rahulremanan/python_tutorial/blob/master/Machine_Vision/01_Transfer_Learning/notebook/Dogs_vs_Cats_Bayesian_classifier.ipynb
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 >=72, to access the course contents.
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 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
• Additional materials: