Classifying images of everyday objects using a neural network

The ability to try many different neural network architectures to address a problem is what makes deep learning really powerful, especially compared to shallow learning techniques like linear regression, logistic regression etc.

In this assignment, you will:

Explore the CIFAR10 dataset: https://www.cs.toronto.edu/~kriz/cifar.html
Set up a training pipeline to train a neural network on a GPU
Experiment with different network architectures & hyperparameters

You might find these notebooks useful for reference, as you work through this notebook:

# Uncomment and run the commands below if imports fail
# !conda install numpy pandas pytorch torchvision cpuonly -c pytorch -y
# !pip install matplotlib --upgrade --quiet

import torch
import torchvision
import numpy as np
import matplotlib.pyplot as plt
import torch.nn as nn
import torch.nn.functional as F
from torchvision.datasets import CIFAR10
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

# Project name used for jovian.commit
project_name = '03-cifar10-feedforward'

import jovian
jovian.commit(project=project_name, environment=None)

[jovian] Detected Colab notebook...
[jovian] Uploading colab notebook to Jovian...
[jovian] Committed successfully! https://jovian.ai/rajibdasbhagat/03-cifar10-feedforward

'https://jovian.ai/rajibdasbhagat/03-cifar10-feedforward'