The Idea of Neural Networks

Last semester I had this very interesting course on Neural Networks and Fuzzy Logic. A big thanks to our professor to make the course as interesting as it sounds.

Do we all not realize our brain is a super computing massively fascinating biological entity. The way we build memories and connect the dots later to figure things out is not at all easy as it sounds. We made computers to do things that we are not good at, like huge mathematical computations. But imagine the power of computers that can learn and make decisions the way humans do. That's what it is! Artificial Intelligence!

Through this post I would straightaway jump into the concepts and the key points. Dividing it into ten sub topics:

1. Sigmoid Neurons
2. Backpropagation Algorithm
3. Training vs Testing Error
4. RBF: Radial Basis Function
5. PCA: Principle Component Analysis
6. Supervised vs Unsupervised learning
7. Classification vs Regression
8. SVM: Support Vector Machine
9. CNN: Convolutional Neural Network
10. Fourier vs Wavelets

Let me first share the doc link in which I have summarized every lecture- https://docs.google.com/document/d/1hVTkZZg0dv_VdqvnsozQwxmCHgarCSnuo0zpEwmV2Tk/edit?usp=sharing

Here I share my scribbles of the sub topics I just mentioned.

I could have made this post very elaborate but I limited myself in just providing the key ideas. I'll share few references here:
http://videolectures.net/DLRLsummerschool2018_toronto/
https://drive.google.com/drive/folders/0B41Zbb4c8HVyUndGdGdJSXd5d3M
https://www.udacity.com/course/intro-to-machine-learning--ud120
http://neuralnetworksanddeeplearning.com/about.html
https://ujjwalkarn.me/2016/08/11/intuitive-explanation-convnets/

This subject is very interesting and the more you explore the more you'll understand. So, enjoy the exploration folks!