How do I train a Convolutional Neural Network (CNN)?

What are the basic steps to train a Convolutional Neural Network?

To train a CNN, you typically follow these steps: 1) Prepare your dataset, 2) Define the CNN architecture, 3) Compile the model, 4) Train the model, and 5) Evaluate its performance.

What kind of dataset do I need for training?

You need a labeled dataset with images that represent the classes you want the CNN to learn. For example, if you're classifying cats and dogs, your dataset should include images of both.

How do I choose the right architecture for my CNN?

Choosing the right architecture depends on your specific task. You can start with popular architectures like VGG, ResNet, or Inception, and then fine-tune them based on your dataset.

What tools or libraries should I use for training?

Common libraries for training CNNs include TensorFlow, Keras, and PyTorch. They provide high-level APIs to simplify the process.