Inspiration
Apple's Face ID !!!
What it does
It takes a data set filled with animals, cars, planes, things, etc, and runs training data through layers of our Convolution Neural Network and outputs its training accuracy.
How I built it
In a team 4 during a Summer College Course, we took our mathmetical artificial intelligence knowledge and transfered in into a real neural network!
Challenges I ran into
Horrendous optimization and network adaptation. Incorrect structuring of CNN layers
Accomplishments that I'm proud of
Implementing a grayscale conversion solution. This was the solution that made this AI work
What I learned
Neural Networks literally work for no reason.
What's next for Image-Recognition-CIFAR-10
Identify harder images! Identify people! Face recognition!
Built With
- jupyter-notebook
- python
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