Implement convolutional neural network (CNN) that can do image classification based on the famous CIFAR-10 dataset.
A Convolutional Neural Network (CNN) is a type of deep learning model primarily used in computer vision tasks, particularly for image recognition, object detection, and image classification.
What makes CNNs powerful for image-related tasks is their ability to automatically and adaptively learn spatial hierarchies of features from the input data. They consist of multiple layers, typically including convolutional layers, pooling layers, and fully connected layers.
In this part we will implement our first convolutional neural network (CNN) that can do image classification based on the famous CIFAR-10 dataset.
We will learn:
- Architecture of CNNs
- Convolutional Filter
- Max Pooling
- Determine the correct layer size
- Implement the CNN architecture in PyTorch