Can you describe the forward(...) function in a neural network?
The forward() function is a method used in neural networks to propagate input data through the network and produce an output. It is an essential component of the feedforward process in which the neural network processes input data through successive layers of interconnected nodes to generate a prediction or classification.

During the forward pass, the input data is fed into the first layer of the network and then passed through each subsequent layer until it reaches the output layer. In each layer, the input is transformed by a series of mathematical operations and activation functions, which adjust the strengths of connections between neurons based on the input data.

The forward() function typically takes in an input tensor and passes it through each layer of the neural network. As the input is propagated through the network, intermediate activations are calculated and stored, which are then used in subsequent layers until the final output is produced.

Overall, the forward() function is a key component of the neural network, as it allows the network to process input data and generate predictions or classifications based on that input.
What is the `backward()` function?
The backward() function is another key method used in neural networks, particularly in the process of training the network. It is essentially the reverse of the forward() function and is used to calculate gradients of the loss function with respect to the weights and biases of the network.

During the training process, the backward() function is used to propagate the error signal from the output layer back through the network to adjust the weights and biases in each layer. This is done through a process called backpropagation, which calculates the partial derivative of the loss function with respect to each weight and bias in the network.

The backward() function is typically used in combination with an optimizer, which uses the gradients calculated by the backward pass to adjust the weights and biases in the network in the direction that minimizes the loss function.

Overall, the backward() function is a crucial component of the neural network training process, as it allows the network to learn from the input data and adjust its weights and biases to improve its predictions or classifications.