What's actually happening to a neural network as it learns?help fund future projects: patreon 3blue1brownan equally valuable form of support. What is backpropagation really doing? here we tackle backpropagation, the core algorithm behind how neural networks learn. if you followed the last two lessons or if you’re jumping in with the appropriate background, you know what a neural network is and how it feeds forward information.
Personally, when i was first learning about backpropagation, i think the most confusing aspect was just the notation and index chasing of it all. but once you unwrap what each part of this algorithm is really doing, each individual effect it's having is actually pretty intuitive, it's just that there's a lot of little adjustments getting. The main goal with the follow on video is to show the connection between the visual walkthrough here, and the representation of these “nudges” in terms of partial derivatives that you will find when reading about backpropagation in other resources, like michael nielsen’s book or chis olah’s blog. video timeline: 0:00 – introduction. Backpropagation is the algorithm for computing the gradient for a single training example. we average all the desired changes for many training examples to compute the gradient for gradient descent…. Review python machine learning: machine learning and deep learning with python, scikit learn, and.
Backpropagation is the algorithm for computing the gradient for a single training example. we average all the desired changes for many training examples to compute the gradient for gradient descent…. Review python machine learning: machine learning and deep learning with python, scikit learn, and. What is backpropagation really doing? | chapter 3, deep learning. uploader: 3blue1brown original upload date: fri, 03 nov 2017 00:00:00 gmt. archive date: wed, 01 dec 2021 03:35:20 gmt. Backpropagation, the topic of this video, is an algorithm for computing that crazy complicated gradient. and the one idea from the last video that i really want you to hold firmly in your mind right now is that because thinking of the gradient vector as a direction in 13000 dimensions is, to put it lightly, beyond the scope of our imaginations.
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