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Backpropagation in Neural Networks for developers
- The article talks about the difficulty of implementing a neural network from scratch using OOP and C# without using python libraries.
- Developers need to understand the math behind neural networks and linear algebra to implement them.
- Backpropagation is difficult to understand and implement, despite being explained as simple in many resources.
- The article proceeds to provide a step-by-step explanation of backpropagation in neural networks.
- It begins by explaining the calculation of the loss and how to find the derivative of the loss function, which is used to update weights and biases.
- The article then goes on to show how the chain rule can be used to calculate the derivative of the loss function for any weight in the neural network.
- It explains how to calculate the derivative for the special weight that connects the input layer to the hidden layer.
- The article also explains how to calculate the derivative for bias, using the same approach as weights.
- The article aims to help developers understand the math behind backpropagation and provide a guide to implementing a neural network from scratch.
- It also recommends taking a break if struggling with the math and encourages developers to keep coding.
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