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
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This video is apart of my Machine Learning Journey course:
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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
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