The more specific your prompt, the more relevant the results. For example, instead of "deep learning," try "convolutional neural networks for image classification."

That's a great strategy! Being specific helps in narrowing down the responses to match exactly what you're looking for. For instance, if you're interested in "convolutional neural networks for image classification," you can explore various aspects like: 1. **Architecture Variants**: Discuss specific CNN architectures like ResNet, Inception, or VGG. 2. **Applications**: Examine how CNNs are applied in areas such as medical imaging, self-driving cars, or facial recognition. 3. **Training Strategies**: Look into techniques like data augmentation, transfer learning, and regularization options. 4. **Performance Metrics**: Analyze how to assess models using metrics like accuracy, precision, recall, and F1-score. 5. **Frameworks and Tools**: Explore libraries like TensorFlow, PyTorch, and Keras that facilitate CNN implementation. Let me know if you'd like to delve into a particular aspect!