Deep learning models are trained using large datasets and techniques such as stochastic gradient descent (SGD) and backpropagation. GPU acceleration is often employed to handle the computational demands of training deep neural networks efficiently.

Deep learning is widely applied across various domains, including computer vision (e.g., image classification, object detection), natural language processing (e.g., machine translation, sentiment analysis), speech recognition, autonomous vehicles, healthcare (e.g., medical image analysis, drug discovery), finance (e.g., fraud detection, algorithmic trading), and robotics.

A solid understanding of linear algebra, calculus, probability, and statistics is beneficial. Programming skills in Python are essential, as most deep learning frameworks are Python-based. Familiarity with basic machine learning concepts is also recommended.

Challenges include the need for large amounts of labeled data, computational resources for training and inference, interpretability of model decisions, and ethical considerations such as bias and fairness. Model performance may degrade with data distribution shifts, requiring continuous monitoring and adaptation.