Using PyTorch to implement a deep learning model - Emmanuel Katto

[AdamHensley]

Great to see you're diving into deep learning with PyTorch! For interpreting your model and understanding feature importance, check out tools like Grad-CAM and Guided Backpropagation. These help visualize which parts of your images your model focuses on. The torchcam library can assist with this. Also, explore Captum, which is designed for model interpretability in PyTorch. It offers algorithms like Integrated Gradients, DeepLIFT, and SHAP values. The documentation and tutorials are beneficial. For more resources, the official PyTorch tutorials and Andrew Ng's Deep Learning Specialization on Coursera are excellent for building a solid foundation.