Backpropagation Explained

The most popular optimization strategy in machine learning is called gradient descent. When gradient descent is applied to neural networks, its called back-propagation. In this video, i'll use analogies, animations, equations, and code to give you an in-depth understanding of this technique. Once you feel comfortable with back-propagation, everything else becomes easier. It uses calculus to help us update our machine learning models. Enjoy! Code for this video: https://github.com/llSourcell/backpropagation_explained Please Subscribe! And like. And comment. That's what keeps me going. Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology instagram: https://www.instagram.com/sirajraval This video is apart of my Machine Learning Journey course: https://github.com/llSourcell/Machine_Learning_Journey More learning resources: https://www.youtube.com/watch?v=XdM6ER7zTLk https://www.youtube.com/watch?v=nhqo0u1a6fw https://www.youtube.com/watch?v=jc2IthslyzM https://www.youtube.com/watch?v=IHZwWFHWa-w https://www.youtube.com/watch?v=umAeJ7LMCfU http://neuralnetworksanddeeplearning.com/chap2.html Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ Sign up for the next course at The School of AI: https://www.theschool.ai And please support me on Patreon: https://www.patreon.com/user?u=3191693