Interpreting deep-learning models is often done using feature attribution. SHAP (SHapley Additional explanations) and LIME (Local Interpretable Model Agnostic Explainations), for example, can be used to determine the importance of individual input features in a model's predictions. Grad-CAM highlights areas in an image that are important for classification and provides a visual description for Data Science Classes in Pune

Another option is to simplify the model. Deep Complex Learning Models can be approximated easily by simpler models. Surrogate models translate the rules of an original model into rules humans can understand without having to examine each neural connection.

It is important to understand the inner workings and applications of deep learning models. The attention visualization and layer-by-layer relevance propagation in transformer architecture models show how neurons prioritize input.

Despite the fact that techniques to improve our ability of interpreting data are useful, there remain some challenges. The interpretations may simplify complex phenomena, leading to a misunderstand. Transparency can be sacrificed to model complexity and this limits the level of insight.

Combining multiple interpretation techniques in practice gives a holistic perspective on model behavior. This leads to better trust, fairness evaluation, and debugging. The research and application of interpretability is crucial as deep learning in sensitive areas such as healthcare and finance has become an important part of decision making.