Deep learning is a subset of machine learning that has achieved success in solving complex problems like speech recognition, natural language processing, and image classification. It's a refinement of artificial neural networks (ANNs), which mimic the human brain's learning process. Deep learning models use multiple hidden layers to break down complex problems into simpler questions, allowing for more accurate predictions. Training deep learning models involves minimizing a cost function, which requires finding the correct set of weights that minimize error. Gradient descent is a common technique used in deep learning, and it works by computing gradients to find the optimal weights. When building a deep learning application, flexible data models, scalability, fault tolerance, and consistency are crucial considerations when selecting a database. Non-relational databases like MongoDB have played a significant role in enabling machine learning and deep learning by providing large volumes of structured and unstructured data.