What is the meaning of forward pass and backward pass in neural networks?
Everybody is mentioning these expressions when talking about backpropagation and epochs.
I understood that forward pass and backward pass together form an epoch.
A forward pass in project management is a technique used to move through a project network diagram. The forward pass helps you understand the project duration and calculate the early start and early finish values (meaning, the earliest day each project task can begin and wrap up).
Backpropagation in neural network is a short form for “backward propagation of errors.” It is a standard method of training artificial neural networks. This method helps calculate the gradient of a loss function with respect to all the weights in the network.
In general, feedforward means moving forward with provided input and weights (assumed in 1st run) till the output. And, backward propagation , as a name suggests, is moving from ouput to input. In BP, we reassign weights based on the loss and then forward propagation runs.
The "forward pass" refers to calculation process, values of the output layers from the inputs data. It's traversing through all neurons from first to last layer. A loss function is calculated from the output values.
The "forward pass" refers to calculation process, values of the output layers from the inputs data. It's traversing through all neurons from first to last layer.
A loss function is calculated from the output values.
And then "backward pass" refers to process of counting changes in weights (de facto learning), using gradient descent algorithm (or similar). Computation is made from last layer, backward to the first layer.
Backward and forward pass makes together one "iteration".
During one iteration, you usually pass a subset of the data set, which is called "mini-batch" or "batch" (however, "batch" can also mean an entire set, hence the prefix "mini")
"Epoch" means passing the entire data set in batches.
One epoch contains (number_of_items / batch_size) iterations
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