How to Calculate Loss, Backpropagation, Gradient Descent on a Simple Neural Network скачать в хорошем качестве

How to Calculate Loss, Backpropagation, Gradient Descent on a Simple Neural Network

End-to-end example of calculating loss, backpropagation, and gradient descent on the most basic neural network. Demonstrated on a whiteboard, with Python code, and simplified math. After this tutorial, you will have a good idea of how neural networks are trained. Demo code: https://github.com/johnnycode8/basic_... Buy Me a Coffee: https://www.buymeacoffee.com/johnnycode 00:00 Intro 00:18 Structure of the 2-Node Neural Network 00:40 Define the Problem (Linear Regression) 02:25 Setup Python to Train the Network 03:02 Start by Guessing the Weight 03:40 Loss (How Bad was the Guess?) 04:26 Loss Function (Mean Squared Error) 04:40 Why Drop Zero from Inputs 05:34 How Weight Affects Loss 06:47 Backpropagation (Derivative of Loss wrt Weight) 11:20 Adjust Weight (Gradient Descent) 11:52 Train Network for y=x 13:12 Purpose of Learning Rate 14:36 Train Network Again for y=-2x 15:32 Add Bias (Intercept) 16:16 Find the Derivative of Loss wrt Bias 17:21 Train Network Again for y=-2x+2 19:01 Burnt-out Cat