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The Little Book of Deep Learning
This book is a short introduction to deep learning for readers with a STEM background. It aims at providing the necessary context to understand landmark AI models for image generation and language understanding.
156 pages, ebook
Published June 1, 2023
About the author
François Fleuret
1 book6 followersFrançois Fleuret is Full Professor and head of the Machine Learning group in the department of Computer Science at the University of Geneva where he holds the chair of machine learning. He received his PhD in Mathematics from INRIA and the University of Paris VI in 2000.
He is the inventor of several patents in the field of machine learning, and co-founder of Neural Concept SA, a company specializing in the development and commercialization of deep learning solutions for engineering design.
He is the inventor of several patents in the field of machine learning, and co-founder of Neural Concept SA, a company specializing in the development and commercialization of deep learning solutions for engineering design.
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Displaying 1 - 13 of 13 reviews
December 25, 2023
Pretty much the best condensed version of what’s going on
March 17, 2024
I did not get any of the math but sometimes it’s useful to be made aware of all the things you don’t understand about a topic, despite basically doing related work on it for years and years.
June 18, 2023
This is a good quick review of Deep Learning ideas and techniques. It helps to have some background in this space as some parts assume knowledge of DL concepts. My favorite part was chapter 5, "Architectures", which described the different DL architectures. The diagrams were excellent and clear. It is a quick read.
The only issue is who is the target audience. For experts they already know everything in here. For novices it assumes too much background. I think it's targeted towards intermediate folks (such as myself) who are in the middle of learning DL. Overall, it's a good review of concepts and introduction to some new concepts I did not know about (Transformers, Attention, etc).
One other thing: the book is a free download from Francois Fleuret's website (https://fleuret.org/francois/). There are a bunch of other great resources on his site! Highly recommended to checkout!
The only issue is who is the target audience. For experts they already know everything in here. For novices it assumes too much background. I think it's targeted towards intermediate folks (such as myself) who are in the middle of learning DL. Overall, it's a good review of concepts and introduction to some new concepts I did not know about (Transformers, Attention, etc).
One other thing: the book is a free download from Francois Fleuret's website (https://fleuret.org/francois/). There are a bunch of other great resources on his site! Highly recommended to checkout!
June 10, 2023
The field is ever evolving and static intros formats are thus disadvantaged, likely this will be a fun historical snapshot towards the end of the year. Thanks for keeping it short
The main contribution of the book, I would say, is leaving our the branches of DL which were losing steam for a while now (e.g. bye, RNN's)
The main contribution of the book, I would say, is leaving our the branches of DL which were losing steam for a while now (e.g. bye, RNN's)
September 7, 2023
This small book explains quite well what happens in the black box of very classic methods like Nonlinear Algebra, Stochastics, Gradient Descent. At the beginning of the book, it briefly explains how a GPU functions.
January 3, 2024
A very short discussion on the state of deep learning till about Mid 2023. It is not a practitioner's guide, nor a introduction-to for lay users. Some technical depth is required.
I can see it being useful if someone is out of regular touch with the field, and wants a quick brush up.
I can see it being useful if someone is out of regular touch with the field, and wants a quick brush up.
August 25, 2023
Short and sweet, maybe serves more as a reference than anything else.
Familiar topics - didn't learn much
Unfamiliar topics - require more context and explanation to fully understand
Familiar topics - didn't learn much
Unfamiliar topics - require more context and explanation to fully understand
December 18, 2023
Only understood half of it but a great introduction to get an idea of the field of AI and the math behind it.
March 3, 2024
A handy guide for Deep learning practitioners. Highly recommended
This entire review has been hidden because of spoilers.
April 11, 2024
Perfect format but too short or shallow
September 15, 2023
Excellent work
Appreciate the structure of this work, which is divided as
(1) Foundation
(2) Deep Models
(3) Application
The Book has helpful illustrations.
Overall, I'd recommend this work to Professionals, Students, and Researchers as a refresher.
Deus Vult,
Gottfried
Appreciate the structure of this work, which is divided as
(1) Foundation
(2) Deep Models
(3) Application
The Book has helpful illustrations.
Overall, I'd recommend this work to Professionals, Students, and Researchers as a refresher.
Deus Vult,
Gottfried
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December 22, 2023The version sold on various platforms such as Amazon, with ISBN 9732346493 is an UNAUTHORIZED COPY of my book. Somebody took the free pdf that I distribute under a non-commercial CC license and sells it. It is an early preprint, sold for 5 times the price of the official version.
The authorized version is available from https://fleuret.org/lbdl as a free phone-formatted pdf, or a $8.50 paperback edition from lulu.com.
The authorized version is available from https://fleuret.org/lbdl as a free phone-formatted pdf, or a $8.50 paperback edition from lulu.com.
Displaying 1 - 13 of 13 reviews