Deep Learning Foundations: Mathematics, Modern Network Practices, and Research Overview

01. Introduction

The article begins with an overview of deep learning, highlighting its importance and the need for solid mathematical foundations.

02. Mathematics and Machine Learning Foundations

This section reviews linear algebra, probability and information theory, numerical computation, and basic machine‑learning concepts that underpin modern deep models.

03. Modern Deep Network Practice

It discusses deep feed‑forward networks, regularization strategies, optimization methods for deep models, convolutional neural networks, and sequence modeling with recurrent and recursive networks, illustrating each topic with visual diagrams.

04. Deep Learning Research

The research part explores linear factor models, auto‑encoders, representation learning, structured probabilistic models, Monte‑Carlo methods, approximate inference, and deep generative models, providing insight into current academic directions.

05. Recommended Reading List

A curated list of essential deep‑learning books is presented, including "Deep Learning" by Goodfellow, Bengio, and Courville, "Python Deep Learning" by François Chollet, and several other notable titles.

Author Introduction

Yao Kaifei, Head of Recommendation Algorithms at Club Factory, shares his background and experience in e‑commerce recommendation systems.

Community and Company Information

The article concludes with details about the DataFun community, recruitment opportunities, and contact information for interested readers.