Can anyone try to implement this in python? Let me know if you need additional info. This is the question. no dataset given as it should work on every dataset.

Write down the code for NN Training and Prediction (20 points) 'It should work for any given data given features and class label Write down code from scratch for a N layer neural network for multiclass classification. You must 1. Use Vectorization - 4 points 2. Just use iterations (no need for convergence condition) 3. Multi-class problem - use softmax and cross entropy - 2 points 4. Code should work for any number of hidden layers provided as input - 4 points 5. Add L2 regularization - 2 points You training function will take as input: X, y, max_iter=1000, alpha=0.01, and hidden_layers=[5, 6, 7] Where hidden_layers=[5, 6, 7] means that your neural network will have 3 hidden layers with first hidden layer havin g 5 hidden units, second hidden layer having 6 hidden units, and 3rd hidden layer having 7 hidden units. 6. Split data into training and test. Plot your error curves for training and validation errors (cost and accuracy) Write down the code for NN Training and Prediction (20 points) 'It should work for any given data given features and class label Write down code from scratch for a N layer neural network for multiclass classification. You must 1. Use Vectorization - 4 points 2. Just use iterations (no need for convergence condition) 3. Multi-class problem - use softmax and cross entropy - 2 points 4. Code should work for any number of hidden layers provided as input - 4 points 5. Add L2 regularization - 2 points You training function will take as input: X, y, max_iter=1000, alpha=0.01, and hidden_layers=[5, 6, 7] Where hidden_layers=[5, 6, 7] means that your neural network will have 3 hidden layers with first hidden layer havin g 5 hidden units, second hidden layer having 6 hidden units, and 3rd hidden layer having 7 hidden units. 6. Split data into training and test. Plot your error curves for training and validation errors (cost and accuracy)