In this tutorial, we present a compact and holistic discussion of Deep Learning with a focus on Convolutional Neural Networks (CNNs) and supervised regression. While there are numerous books and articles on the individual topics we cover, comprehensive and detailed tutorials that address Deep Learning from a foundational yet rigorous and accessible perspective are rare. Most resources on CNNs are either too advanced, focusing on cutting-edge architectures, or too narrow, addressing only specific applications like image classification.This tutorial not only summarizes the most relevant concepts but also provides an in-depth exploration of each, offering a complete yet agile set of ideas. Moreover, we highlight the powerful synergy between learning theory, statistic, and machine learning, which together underpin the Deep Learning and CNN frameworks. We aim for this tutorial to serve as an optimal resource for students, professors, and anyone interested in understanding the foundations of Deep Learning. Upon acceptance we will provide an accompanying repository under \href{https://github.com/neoglez/deep-learning-tutorial}{https://github.com/neoglez/deep-learning-tutorial} Keywords: Tutorial, Deep Learning, Convolutional Neural Networks, Machine Learning.

本教程针对深度学习中的卷积神经网络（CNN）和监督回归进行了全面而紧凑的讨论，填补了现有资源中基础而严谨的教程空缺。文章强调学习理论、统计学与机器学习之间的强大协同作用，旨在为学生和教授提供了解深度学习基础的理想资源。该工作展示了对深度学习概念的深入解析，提高了学习的可及性和系统性。

深度学习与卷积神经网络：聚焦监督回归的紧凑全面教程（预印本）