Please forgive me as I am new to this. I have attached a diagram trying to model my understanding of neural network and Back-propagation? From videos on coursera and resources online I formed the following understanding of how neural network works:
- Input is given which get's weight assigned to it using a probability distribution
- The activation functions use the weights to provide the predicted value
- The cost or loss functions calculate the error of the prediction between the actual class and the predicted value
- The optimization functions such as gradient decent use the results of the cost function to minimize the error.
If the above is correct then I am struggling to understand the connection between Gradient Descent and Backpropagation ?
Here is an image of my understanding so far:
Many thanks in advance.