How do I train a convolutional neural network?

How do I train a convolutional neural network?

To train a convolutional neural network (CNN), you need a labeled dataset, a model architecture, and a training algorithm.

What kind of dataset do I need?

You need a dataset with images and corresponding labels. For example, if you're classifying cats and dogs, each image should be labeled as 'cat' or 'dog'.

How do I choose the architecture for my CNN?

Start with a simple architecture, like a few convolutional layers followed by pooling layers. You can experiment with more complex architectures later.

What training algorithm should I use?

Common algorithms include Stochastic Gradient Descent (SGD) and Adam. Adam is often preferred for its adaptive learning rate.

How do I evaluate the performance of my CNN?

You can evaluate performance using metrics like accuracy, precision, and recall on a validation dataset that the model hasn't seen during training.