The backpropagation algorithm is the fundamental building block of neural networks and is used to effectively train them through the chain rule method- a technique used to find the derivatives of cost, considering any variable in a nested equation. While most packages already contain backpropagation algorithms in them, knowing the math behind them and how they work will help you better understand more advanced algorithms as well as handle vanilla ones with ease. Check out these free short pdf course notes on the backpropagation algorithm to learn some of the useful formulas and finding the results for backpropagation for the output layer and hidden layer.
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Who is it for
These course notes are designed for Machine Learning Engineers, Data Scientists and anyone who is eager to utilize the potential of artificial intelligence, machine learning and deep learning models in their work .
How it can help you
Learning the math behind the backpropagation chain rule will give you an edge over others who simply rely on the in-built functions of their respective libraries and take your neural network models to the next level.
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