[{"data":1,"prerenderedAt":90},["ShallowReactive",2],{"tags-beginners":3},{"blogs":4,"videos":5,"total":89},[],[6],{"id":7,"title":8,"body":9,"date":75,"description":76,"extension":77,"meta":78,"navigation":79,"path":80,"seo":81,"stem":82,"tags":83,"youtubeId":87,"__hash__":88},"videos\u002Fvideos\u002Fintro-neural-networks.md","But What Is a Neural Network?",{"type":10,"value":11,"toc":68},"minimark",[12,17,26,30,46,50,53],[13,14,16],"h2",{"id":15},"about-this-video","About this Video",[18,19,20,21,25],"p",{},"This is the first video in 3Blue1Brown's ",[22,23,24],"em",{},"Deep Learning"," series. It builds an intuitive understanding of how neural networks learn without assuming any prior ML knowledge.",[13,27,29],{"id":28},"what-youll-learn","What You'll Learn",[31,32,33,37,40,43],"ul",{},[34,35,36],"li",{},"What neurons and layers are",[34,38,39],{},"How a network transforms input data through layers",[34,41,42],{},"The idea of learning as adjusting weights and biases",[34,44,45],{},"Why depth (multiple layers) matters",[13,47,49],{"id":48},"notes","Notes",[18,51,52],{},"Watch this before touching any ML framework. The visual intuition from this video makes gradient descent, backpropagation, and loss functions much easier to understand later.",[18,54,55,59,60,67],{},[56,57,58],"strong",{},"Next",": Watch the follow-up on ",[61,62,66],"a",{"href":63,"rel":64},"https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=IHZwWFHWa-w",[65],"nofollow","gradient descent",".",{"title":69,"searchDepth":70,"depth":70,"links":71},"",2,[72,73,74],{"id":15,"depth":70,"text":16},{"id":28,"depth":70,"text":29},{"id":48,"depth":70,"text":49},"2026-03-25","3Blue1Brown's classic visual introduction to neural networks — the best starting point for anyone new to deep learning.","md",{},true,"\u002Fvideos\u002Fintro-neural-networks",{"title":8,"description":76},"videos\u002Fintro-neural-networks",[84,85,86],"neural-networks","beginners","3blue1brown","aircAruvnKk","lJsWJ2lWK0q3S0RJB26Z6aS5c18H3ytUqf2hdbFtLgs",1,1776276582892]