Title: Image Classification with Deep Learning
This project will make use of the dataset provided by the Kaggle competition: Dog Breed Identification.
This course provides a comprehensive introduction to image classification using deep learning techniques. It covers the fundamental concepts of Convolutional Neural Networks (CNNs), hands-on training with custom CNNs, and advanced techniques like transfer learning with pre-trained models such as ResNet34 and YOLOv8.

Module 1: Convolutional Neural Networks (CNNs)
In this module, you will learn the basics of Convolutional Neural Networks, the building blocks of modern image classification systems. We will cover the architecture of CNNs, including convolutional layers, pooling layers, activation functions, and fully connected layers. By the end of this module, you'll understand how CNNs work and why they are so effective for image classification tasks.

Module 2: Training with Custom CNN
This module focuses on designing, building, and training your own custom CNN for image classification. You will learn how to preprocess image data, construct a CNN architecture from scratch, and train the model on a dataset. We will also cover techniques for improving model performance, such as data augmentation, regularization, and optimization strategies. By the end of this module, you'll be able to create and train a custom CNN model for any image classification problem.

Module 3: Transfer Learning with ResNet34 and YOLOv8
In this advanced module, you will explore the powerful technique of transfer learning, which allows you to leverage pre-trained models to enhance your image classification tasks. We will use ResNet34, a state-of-the-art deep learning model, to demonstrate how transfer learning can be applied to achieve high accuracy with less training data. Additionally, you'll learn about YOLOv8, a cutting-edge model for object detection, and how its principles can be applied to image classification tasks. By the end of this module, you'll have the skills to implement transfer learning using ResNet34 and YOLOv8 for efficient and accurate image classification.