Numpy

This is to implement backpropagation algorithm in numpy which would help me to further understand how this works. import pandas as pd import numpy as np from pdb import set_trace from sklearn import datasets Design the network structure Each layer contains the weights/bias and activation union structures = [ {"input_dim": 2, "output_dim": 25, "activation": "relu"}, {"input_dim": 25, "output_dim": 50, "activation": "relu"}, {"input_dim": 50, "output_dim": 50, "activation": "relu"}, {"input_dim": 50, "output_dim": 25, "activation": "relu"}, {"input_dim": 25, "output_dim": 1, "activation": "sigmoid"}, ] Initiate the parameters The weights can be random number and bias are preferred to be small postive values in order to pass the relu in the beginning....