How Computers Learn | Neural Networks Explained (Gradient Descent & Backpropagation)

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In the last video in this series, we went through an intuitive example, starting from scratch and layer-by-layer hand-building a neural network that can classify patterns in an input image.

The focus of this video then will continue right were the last one left off, as we delve into how these networks are actually able to ‘learn’.

00:00 Intro
0:27 How Computers Learn

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Sources ➤

[1]https://www.cs.toronto.edu/~hinton/absps/NatureDeepReview.pdf
[2]https://www.youtube.com/watch?v=ILsA4nyG7I0 (How Deep Neural Networks Work – Brandon Rohrer)
[3]https://www.youtube.com/watch?v=aircAruvnKk (But What *is* A Neural Network? | Deep Learning, Chapter 1 – 3Blue1Brown)
[4]https://www.youtube.com/watch?v=IHZwWFHWa-w (Gradient Descent, How Neural Networks Learn | Deep Learning, Chapter 2 – 3Blue1Brown)
[5]https://medium.com/the-theory-of-everything/understanding-activation-functions-in-neural-networks-9491262884e0
[6]https://machinelearningmastery.com/rectified-linear-activation-function-for-deep-learning-neural-networks/
[7]https://medium.com/@devnag/machine-learning-representation-evaluation-optimization-fc7b26b38fdb
[8]https://www.youtube.com/watch?v=bVQUSndDllU (Neural Networks [E01: Introduction] – Sebastian Lague)
[9]https://www.youtube.com/watch?v=Ilg3gGewQ5U (Backpropagation Calculus | Deep Learning, Chapter 4 – 3Blue1Brown)

Producer ➤ Ankur Bargotra

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Видео How Computers Learn | Neural Networks Explained (Gradient Descent & Backpropagation) канала Futurology — An Optimistic Future