A selective overview of deep learning.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Statistical Learning Theory

Background:

• Deep learning models, built on compositions of nonlinear functions, have demonstrated significant advancements.
• Recent progress has dramatically enhanced performance in areas like computer vision and natural language processing.

Purpose of the Study:

• To define deep learning from a statistical viewpoint.
• To compare deep learning characteristics with classical methods.
• To explore the theoretical foundations of deep learning.

Main Methods:

• Introduction of common neural network architectures (e.g., convolutional neural nets, recurrent neural nets, generative adversarial nets).
• Explanation of training techniques (e.g., stochastic gradient descent, dropout, batch normalization).
• Statistical analysis of deep learning models and training.

Main Results:

• Highlighting key deep learning characteristics such as depth and over-parametrization.
• Discussing the practical and theoretical advantages offered by these characteristics.
• Reviewing current theoretical results, acknowledging their suggestive nature.

Conclusions:

• A comprehensive understanding of deep learning is still evolving.
• The study provides perspectives to stimulate further statistical research into deep learning.
• Deep learning's success is linked to its ability to model complex dependencies through layered nonlinear functions.