TOPICS COVERED IN THIS LECTURE
This lecture covers Computer Vision, Neural Network Architectures, Convolutional Neural Networks (CNN), Object Detection, Overfitting, and Data Augmentation.
There’s a famous scene in the hit TV show Silicon Valley where Jian-Yang, a software developer, demos an app that can identify any kind of food from a picture.
Unfortunately it classifies everything as “Hotdog” and “Not hotdog” 😅
The show is fiction, but did you know it’s very easy to build an object classifier yourself?
And you’re not limited to only a single hotdog category either…
And you can do everything in C#!
In this lecture, I’ll show you how easy it is to build a classifier that can identify dogs and cats. I’ll build the neural network from scratch, train it on 2000 images of dogs and cats, and test my app live.
Check it out!
– Introduction: who is Mark?
– The robots are coming!
– Save your future and learn to build robots
– Key deep learning algorithms to master
– The sandwich model of neural network architectures
– Introducing the convolutional neural network (CNN)
– The training data we’re going to use
– C# code walkthrough
– Correcting overfitting with Data Augmentation
– Testing the neural network
– Questions & answers
‘Never get tired of seeing this!’
‘Amazing 👏 You rock! :D’
‘Funtastic! Thank you, Mark’
‘I’m impressed :)’
‘Super excited.. love to see the c# code’