Meivan
Neural Networks Backpropagation from Scratch: A Step-by-Step Guide
Backpropagation is the cornerstone of training neural networks. In this post, we’ll implement backpropagation from scratch in Python, demystifying how neural networks learn.
Why Backpropagation?
Backpropagation allows us to compute the gradient of the loss function with respect to each weight in the network efficiently. This is essential for training neural networks using optimization algorithms like gradient descent.
Step 1: Define the Network Architecture
We’ll start by defining a simple feedforward neural network with one hidden layer.
Step 2: Forward Pass
In the forward pass, we compute the output of the network given an input. We’ll use activation functions like ReLU for the hidden layer and softmax for the output layer.
Step 3: Compute Loss
Next, we calculate the loss using a suitable loss function, such as cross-entropy for classification tasks.
Step 4: Backward Pass
Now comes the backpropagation step. We’ll compute the gradients of the loss with respect to each weight in the network by applying the chain rule of calculus.
Step 5: Update Weights
Finally, we’ll update the weights using an optimization algorithm like stochastic gradient descent.
Conclusion
By implementing backpropagation from scratch, we gain a deeper understanding of how neural networks learn and can better troubleshoot and optimize our models.