CIFAR-10 Image Classification using PyTorch | Kaggle project

In this tutorial, we delve into a comprehensive deep learning project on image classification using PyTorch. We start by importing necessary libraries and dependencies, including Py7zr for handling compressed files and PyTorch for building and training our convolutional neural network (CNN) model. The dataset we use is the CIFAR-10 dataset, which consists of 60,000 32x32 color images in 10 classes, with 6,000 images per class. The video covers: Importing and preprocessing the CIFAR-10 dataset. Splitting the dataset into training and validation sets. Building and training a ResNet-18 CNN model for image classification. Evaluating the model's performance using various metrics like accuracy, confusion matrix, precision-recall curves, and ROC curves. Creating a submission file for test images. This tutorial provides a step-by-step guide for beginners to understand how to implement a deep learning project for image classification using PyTorch. Whether you're new to deep learning or looking to expand your skills, this tutorial will help you grasp the fundamentals of building and training CNN models for image classification tasks. Subscribe for more deep learning tutorials and don't forget to like and share this video if you find it helpful! Download Code file - https://www.4shared.com/s/fUfHM2YeEge Kaggle dataset - https://www.kaggle.com/competitions/cifar-10 Kaggle Working Notebook - https://www.kaggle.com/code/manojajj/resnet-18-cnn #deeplearning #pytorch #imageclassification #cnn #tutorial #machinelearning #computervision #datascience #artificialintelligence #neuralnetworks #dataanalysis #programming #datapreprocessing #modelevaluation #datavisualization #classification #CIFAR10 #ResNet #dataprocessing #modeltraining #modelevaluation #dataanalysis #pythonprogramming #datavisualization #deeplearningtutorial #ai #dl #ml #tutorialvideo #youtubetutorial #kaggle