Deep Learning with CNNs: A Compact Holistic Tutorial with Focus on Supervised Regression (Preprint)

TL;DR

This tutorial provides a comprehensive, accessible overview of Deep Learning with CNNs and supervised regression, emphasizing foundational concepts and the synergy between learning theory, statistics, and machine learning.

Contribution

It offers a complete, rigorous yet accessible tutorial on CNNs and supervised regression, filling a gap in comprehensive foundational resources.

Findings

Summarizes key CNN concepts and supervised regression techniques.

Highlights the integration of learning theory, statistics, and machine learning.

Provides an open-source repository for further learning.

Abstract

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.