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 gets 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 descent 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: