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"""
1. Creating a deep neural network with hidden layers
2. Using a non-linear activation function
3. Using a GPU (when available) to speed up training
4. Experimenting with hyperparameters to improve the model
"""
'\n1. Creating a deep neural network with hidden layers\n2. Using a non-linear activation function\n3. Using a GPU (when available) to speed up training\n4. Experimenting with hyperparameters to improve the model\n'

Import Libraries

import torch
import torchvision
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import torch.nn as nn
import torch.nn.functional as F
from torchvision.datasets import MNIST
from torchvision.transforms import ToTensor
from torchvision.utils import make_grid
from torch.utils.data.dataloader import DataLoader
from torch.utils.data import random_split
%matplotlib inline

# Use a white background for matplotlib figures
matplotlib.rcParams['figure.facecolor'] = '#ffffff'

Load datasets

dataset = MNIST(root='data/', download=True, transform=ToTensor())