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[D] Explaining Feedforward, Backpropagation and Optimization: The Math Explained Clearly with Visualizations. I took the time to write this long article (>5k words), and I hope it helps someone understand neural networks better.

I have been studying Machine Learning in the last few months, and I wanted to really get to understand everything that goes on in a basic neural network (excluding the many architectures). Therefore, I took the time to write this long article, to explain what I have learned. In particular, the post on purpose very extensive and goes into the smaller details; this is to have everything in one place. As the site says, it is machine learning from scratch, and I share what I have learned.

The particular reason for posting here, is that I hope someone else could learn from this. The goal is to share the knowledge in the easiest absorbable way possible. I tried to visualize much of the process going on in neural networks, but I also went through the math, to the detail of the partial derivatives.

This was quite a journey, and it took about 1 month to read all the things I have read, and write it down, have it make sense and creating the graphics.

Regardless, here is the link. Any constructive feedback is appreciated.

https://mlfromscratch.com/neural-networks-explained/

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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.