Neural Networks Why have multiple Layers in Neural Networks?

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Unlock the power of multi-layer neural networks! In this video, we explore the concept of having multiple layers in artificial neural networks and why they are crucial for solving complex problems. From the basics of feedforward networks to advanced deep learning models, you'll learn about the different types of layers and their uses. With clear explanations and practical examples, this video is a must-watch for anyone interested in the field of machine learning and AI. So, whether you're a computer science student, a data scientist, or just curious about how machines learn, don't miss this opportunity to deepen your understanding!

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What a good explanation! The whole video, the timing and the talk are so well suited to transmit the idea in the simplest possible way. So the number of parameters is much bigger that the number of neurons because in every layer you are adding all the inputs * weights plus biases... very clever approach. And the reason you use non linear functions is that they contains much more information as the linear ones. Then using derivatives you can approximate the non linear to linear, I asked Bard in this regard and it told me that this is done with Taylor series. I hope it's not lying to me 😂 Anyway, I'm starting to understand the very basics of this stuff. Thanks so much! (Please let me know if I'm getting the idea the right way) Regards :)

JorgeMartinez-xbks