Deep Learning ressources

This file is a list of recommended deep learning ressources. I have not reviewed all ressources completely, but I have at least briefly checked if the ressource is reliable. Please get in touch with me, if you have concerns or further sources. The list is updated from time to time, but unfortunately it will never be up to date as the field is evolving too fast (see thereisanaiforthat.com¹)

Table of contents

• Table of contents
• Books
• Courses/Slides
• Online courses
• Videos
• Podcasts
• Other ressources

Books

• Computer Vision: Algorithms and Applications, Richard Szeliski, In the 2nd Edition: Chapter 5 link

• Mathematics for Machine Learning, Deisenroth, A. Aldo Faisal, and Cheng Soon Ong. link

• Deep Learning – A visual approach/From basics to practice, Andrew Glassner, link

Courses/Slides

• Introduction to Computer Vision, CS5670, Spring 2021, Cornell Tech, Noah Snavely and many more: Yung-Yu Chuang, Fredo Durand, Alexei Efros, William Freeman, James Hays, Svetlana Lazebnik, Andrej Karpathy, Fei-Fei Li, Srinivasa Narasimhan, Silvio Savarese, Steve Seitz, Richard Szeliski, and Li Zhang. link

• Convolutional Neural Networks for Visual Recognition, CS231n, Stanford, Fei-Fei Li, Andrej Karpathy, Justin Johnson, Ranjay Krishna, Danfei Xu link

• Recognizing and Learning Object Categories, Awarded the Best Short Course Prize at ICCV 2005, Li Fei-Fei (Stanford), Rob Fergus (NYU), Antonio Torralba (MIT) link

• Introduction to Machine Learning, Google Developers Crash Course, Peter Norvig, link

• Introduction to deep learning, MIT 6.S191, MIT’s introductory program on deep learning methods with applications in computer vision (and others), and more!, link

• Deep Learning in Computer Vision, York University, Prof. Kosta Derpanis, introductory course with lecture videos and slides as well as additional links and ressources, link

Online courses

• Generative AI learning path, Google Cloud Skills Boost, a bunch of courses about generative AI with the focus on Google’s Generative AI Studio, link

• Deep Learning Courses at Coursera

Videos

Please note that this list is order by length, not by importance.

• Yann LeCun: Artificial intelligence, revealed, link - High level introduction with short animations (15 min)

• Geoffrey Hinton: The Foundations of Deep Learning, link - A high level rather general and entertaining introduction (28 min)

• Luis Serrano: A friendly introduction to Deep Learning and Neural Networks, link –> Visual introduction with simple examples (33 min)

• Grant Sanderson (3blue1brown): Neural networks playlist, link - Beautifully animated basic math explanation (65 min)

• Shree Nayar: First principles of Computer Vision, link –> Perception –> Neural Networks, rather mathematical introduction (~1,5 h)

• Andrew Glassner: Deep Learning Crash Course at Siggraph 2018, link –> Visual introduction (~3 h)

• Rachel Hu: Computer Vision - Machine Learning University, link –> Lecture series starting from machine learning with Computer Vision as main use case, supported by Amazon (~5 h)

• Andrej Karpathy (a.o.): Stanford lecture CS231n from winter 2016, link –> Practical introduction to software development (~20 h)

• Justin Johnson: Michigan State University course on Deep Learning for Computer Vision from 2020, link –> Further development of the Stanford course (~24 h)

Podcasts

• The Robots Brains, https://www.therobotbrains.ai/
• Lex Fridman Podcast, #333 – Andrej Karpathy: Tesla AI, Self-Driving, Optimus, Aliens, and AGI, https://lexfridman.com/andrej-karpathy/

Other ressources

• ML YouTube Courses and Machine Learning Course Notes by DAIR.AI

• Visual explanations of core machine learning concepts, A bunch of interactive web pages the introduce various topics, provided by the Machine Learning University (Amazon)

Author: Uwe Hahne

Last update: January 2025