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Updated 3 years ago
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, we 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
Notebooks useful for reference:
# 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'