Classical Computer Vision + lessons learned from DeepLearning.ai

This is a past event

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Details

We will discuss classic computer vision algorithms implemented in these two videos:
https://youtu.be/WzbgZtzRC1k
https://youtu.be/jo9zpY3WC20

Essentially classic computer vision, feature engineering and SVMs. Why are they effective and what are their real shortcomings.

We will also discuss lessons learned from Andrew Ng's Deep Learning AI series of courses on Coursera.

Edit:
1- Guys on the waiting list, if it's just two people I suggest you attend, we will try to squeeze into the room (which is pretty big)

2- Git repos:

here's the udacity starter kit for term 1:
https://github.com/udacity/CarND-Term1-Starter-Kit

here are the two project git repos:
https://github.com/udacity/CarND-LaneLines-P1
https://github.com/udacity/CarND-Vehicle-Detection

my solutions:
https://github.com/lets-learn-AI/advanced-lane-detection
https://github.com/lets-learn-AI/vehicle-detection-svm

here's Udacity image detection done right using Yolo: https://github.com/udacity/self-driving-car/tree/master/vehicle-detection/darkflow

(note that Yolo was covered in one of the courses by Andrew Ng and we will discuss that)

Edit 2:
- Guys the meetup is what we make of it. If people come prepared, it will be a workshop session around getting the first classical CV and ML projects working.

- If anyone can't attend please change your RSVP status.