Why Neural Networks Need Layers

Why do neural networks need to be deep? In this video we explore how neural networks transform perceptions into concepts. This video unravels the mystery behind how machines interpret input data, such as images or sounds, and categorize them into recognizable concepts. From the basic structure of neurons and layers to the intricate play of weights and activations, get a comprehensive understanding of the learning process. Explore real-world applications like handwriting recognition and how layered processing aids in effective data categorization. Whether it's distinguishing between summer and winter days based on temperature and humidity or recognizing handwritten digits, the magic lies in the layered architecture of neural networks. This video elucidates how these artificial networks mimic the human brain's ability to interpret, recognize, and reason, marking a significant stride in AI research towards creating machines capable of reasoning. Why layers matter.

[ArtOfTheProblem]

STAY TUNED: Next video will be on "History of RL | How AI Learned to Feel"
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[JuergenAschenbrenner]

Probably the most intuitive accessible explanation of layering I could find so far

[GrahamTodd-ca]

I don't mind that you take your time making these. Your meticulous script preparation & attention to production values allow you to pack massive amounts of information into these videos. You are creating "aha!" moments & rewiring neurons around the world. Bravo!

[NelsonIngersoll]

I know a little about computers. Used to be a lot; but, then I retired and computers and computing move on. This was a wonderful explanation. Not too fast, not in the least boring, and I learned some things. Thank you and KUDOS!

[jamesallen]

Let's all be real here, that last layer is really just on LSD. That's how it all works. Those were some trippy images.

Joking aside, fantastic video!

[hafty]

never would have imagine this stuff in this way. the patience and care of thought behind it is just, like, therapeutic to take in. million thanks man

[the.afronautz]

This may be the single best video describing the basics of neural network, at least the forward propagation stage. Would love to see this same style applied to explaining loss, back propagation, and gradient descent. Incredible work.

[MohkKh]

Sorry for my English
I registered for this channel many years ago and waited eagerly for videos.

[MRKS]

hey keep going with the videos. The quality of your vids easily justifies 2M subs -- you’ll blow up eventually

[heidtmare]

Even though I've seen these concepts before this video does a great job of slowly building up the ideas and bringing the viewer along to the next level of understanding.
This was very good. Thank you for taking the time and effort to put this together.

[savagecabbage]

i prefer your videos over 3b1b. you include a variety of backgrounds/contexts to help me pay more attention (and not get stuck to the monotone black bg with animations). thank you!!!

[Virus]

Sometimes I wish YouTube had a super-like button or something to express how much I like this

[RokoThEMaster]

Your videos are a thing of beauty! The attention to detail is fascinating, especially how it clarifies the concepts that are explained. I can only imagine how beautiful the world would be if everything was explained in this manner!

[mangopomelo]

i'm actually from khan academy, i had never thought i can find such an impressive video just because i clicked a link. fascinating!

[vicuppal]

It was fascinating to see the images when probing the different layers. The paper folding example was great at explaining this at least for me.

[tolex]

Wow...! This was clearly the best ever explanation of neural networks I’ve ever seen! For awhile I even thought I understood them... ;-) great vid, thx!

[Pakalaakhil]

I'm from accounting field. Randomly got this video from Reddit. I have to tell you, your explanation and way of presenting is not just good, it's interesting too.plase continue doing what you are doing.

[iberiaaydin]

You sir, you deserve much more attention. Very well illustrated and clearly explained. Thanks.

[hmm]

beautifully crafted... we can see the hardwork you have put into it.. subbed

[vedhasp]

I think there is a typo at 5:15. Active and inactive should be flipped for any 1 line drawn for consistency. If the circles represent 'active' data points, the active-inactive labels for the slant line at the right should be flipped.