Deep Learning (DL) is a subset of Machine Learning that involves using hierarchical neural networks to extract complex features from data, with networks like Deep Belief Networks, Convolutional Neural Networks, and Recurrent Neural Networks being notable examples. Unlike traditional "shallow" learning, where features are manually selected, DL systems automatically learn features through processes such as back propagation across multiple layers, enabling them to recognize high-level patterns more effectively. Although theoretically, a single-layer perceptron can solve any problem, DL's advantage lies in its ability to use fewer neurons across multiple layers to achieve the same results, avoiding the impracticality of using an exponentially large single layer. However, DL presents challenges such as the need for large datasets, significant computational power, and difficulties in training and optimization. Vendors like Clarifai can help overcome these hurdles by providing access to extensive datasets, powerful cloud-based computational resources, and expert teams to enhance model performance, thereby simplifying the process of building effective DL models.