Understanding Backpropagation 🐸
Backpropagation is one of the most popular algorithms used for training artificial neural networks, especially in deep learning. It is essential to understand backpropagation if you want to delve into the world of neural networks.
At the core, backpropagation is all about optimization – finding the minimum of the loss function that indicates the error in the predictions of the network. It does so by updating the weights iteratively in the opposite direction of the gradient of the loss with respect to the weights.
Key Concepts
- Forward Pass: Calculating the output of a network given an input.
- Loss Function: A measure of how well the network's predictions match the actual data.
- Gradient Descent: An optimization technique to minimize the loss by adjusting weights using gradients.
To dive deeper, check out our detailed tutorial on Neural Networks.