README.md

Artificial Neural Networks (ANNs)

Artificial Neural Networks (ANNs) is a group of neurons.
A neuron is a small algorithm that behaves on data the way that he learn to behaves on. The way he behaves on data is unique, when there are more neurons on 1 network the way he behaves on data can been learned to be very precisely on data without programming the algorithm to do so.
By giving the algorithm data with the label as result it have the change to learn himself to give the answer on unknown labeled data.

Artificial Neural Networks is a filter that keeps information, For example you get some filter and you are filtering paint. The first time you set white paint in the filter, than you check is the outcoming color is correct with the color that the filter has in his output. If the result is correct than do NOT clean the filter for next color. Than the next color will be black, the outcomming result with the white of the filter makes it gray color this is almost correct so clean 50% of the filter untill you get the correct output of the filter you wanted.

When you have more filters we can interact on different colors. When we set filters after the output of a filter we can make the filter more flexible when they all have there unique filtering thinkned on the color. This makes it dynamic in in filtering very specific things.

Subsets of Artificial Neural Networks:

best way is to learn is from TOP to BOTTOM.

• Perceptron
• Feed Forward
• Radial Basis Network
• Deep Feed Forward

Usefull Resources:

• https://pytorch.org/tutorials/beginner/pytorch_with_examples.html
• https://www.investopedia.com/terms/a/artificial-neural-networks-ann.asp
• http://neuralnetworksanddeeplearning.com/chap3.html
• https://cs231n.github.io/neural-networks-1/
• https://www.youtube.com/watch?v=CqOfi41LfDw
• https://www.youtube.com/watch?v=IHZwWFHWa-w&list=RDCMUCYO_jab_esuFRV4b17AJtAw&index=1
• https://mlfromscratch.com/neural-networks-explained/#/
• https://medium.com/@b.terryjack/introduction-to-deep-learning-feed-forward-neural-networks-ffnns-a-k-a-c688d83a309d
• https://www.analyticsvidhya.com/blog/2016/03/introduction-deep-learning-fundamentals-neural-networks/
• https://www.youtube.com/watch?v=gmjzbpSVY1A&list=PLQVvvaa0QuDcjD5BAw2DxE6OF2tius3V3&index=1
• https://medium.com/@b.terryjack/introduction-to-deep-learning-feed-forward-neural-networks-ffnns-a-k-a-c688d83a309d
• https://medium.com/coinmonks/backpropagation-concept-explained-in-5-levels-of-difficulty-8b220a939db5
• https://page.mi.fu-berlin.de/rojas/neural/chapter/K5.pdf