A Selective Overview of Deep Learning

Comprehensive overview of key concepts and recent advancements in deep learning, covering neural network models, training techniques, and theoretical foundations.

Introduction

A Selective Overview of Deep Learning by Fan, Ma, Zhong provides a statistical and scientific perspective on the key concepts and recent advances in deep learning. It introduces common neural network models and training techniques, highlights the new characteristics of deep learning, and discusses the theoretical foundations of this powerful machine learning approach.

Highlights

• Covers common neural network models such as convolutional neural networks, recurrent neural networks, and generative adversarial networks
• Explains training techniques like stochastic gradient descent, dropout, and batch normalization
• Highlights the new characteristics of deep learning, including depth and over-parametrization, and their practical and theoretical benefits
• Samples recent results on the theories of deep learning, providing insights into this rapidly evolving field

Recommendation

This course is recommended for anyone interested in understanding the statistical and scientific foundations of deep learning. It provides a comprehensive overview of the key concepts and recent advancements, making it valuable for both beginners and experienced practitioners in the field of machine learning and artificial intelligence.