SKU: 39974680607

Superlift 87283 SL Shadow Shock Absorber - 22.92 Ext 13.92 Col (w/ Stem Upper Mnt/Eye Lower Mnt)

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Superlift 87283 SL Shadow Shock Absorber - 22.92 Ext 13.92 Col (w/ Stem Upper Mnt/Eye Lower Mnt)Established in 1975, Superlift remains a pioneer in the industry, and is one of the few suspension companies operated by true truck and off road enthusiasts. We are passionate about trucks, wheeling and an outdoor lifestyle that go hand in hand. We can talk the talk because we walk the walk. This question. ''What's best for the customer?'' is always at the top of our list in everything we do, from product planning and design to customer service. This

Established in 1975, Superlift remains a pioneer in the industry, and is one of the few suspension companies operated by true truck and off-road enthusiasts. We are passionate about trucks, wheeling and an outdoor lifestyle that go hand-in-hand. We can talk the talk because we walk the walk.This question.... ''What's best for the customer?''... is always at the top of our list in everything we do, from product planning and design to customer service. This simple guiding principal has served us well for decades, and will continue to guide us into the future.We are proud to provide the truck, Jeep and SUV owner products that perform better than advertised. We understand that most people spend the majority of their windshield time on-road, and our designs reflect this. You will be 100% satisfied with our products on-road or off - we guarantee it! Determining what product is right for your vehicle and lifestyle can be a complicated task, especially with so many companies and options to choose from. We urge you to carefully research your purchase. Most lift kits represent a considerable investment, and you will literally live with your choice for years. Talk to fellow off-roaders, and your local automotive specialty shop before making a purchase. We love to talk shop, and we encourage you to give our tech staff a call. Bottom line... if you do your homework, you'll discover that we've done ours.

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SKU: 39974680607

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4.9 ★★★★★
Based on 15 reviews
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Shannon
Whiting, US
★★★★★ 5
The best DL/ML book I have ever seen!!
Format: Hardcover
Fantastic deep-learning book! The logic is very easy to follow, but the content is very thorough when it comes to explaining the theories behind it, making it perfect for beginners as well as math and CS students. The best DL/ML book I have ever seen!!
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Reviewed in the United States on November 30, 2025
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Verified Purchase
William P Ross
Louisville, US
★★★★★ 5
Comprehensive Look At An Incredibly Complex Topic
Format: Hardcover
Deep Learning is an advanced book with great explanations and details. There is a heavy math focus with the book's beginning chapters detailing the necessary linear algebra and probability that one will need to understand deep learning. I liked that the author's chose to cover only the parts of these subjects which are relevant to deep learning. There are many interesting philosophical sections in the book as well. Just about when I was feeling overwhelmed with the complexity of the mathematics the authors take a step back and cover the foundations of deep learning such as borrowing concepts from human learning. There was an interesting dicussion about the early studies done on the vision of cat's and monkey's in the 1970s. The text covers the entire history of deep learning and the bibliography is hundreds of sources. It is clear this is the most comprehensive text available about deep learning. For anybody interested in this topic this book is a mandatory read. There are sections about machine learning as well, which makes sense because deep learning is a subset of machine learning. These sections focused on the machine learning concepts which are most relevant to deep learning. The book was well organized and divided into three parts which cover mathematics related to deep learning, typical deep learning techniques, and then more experiment learning techniques. Often the author's state when a technique works well or when it does not, and which types of data works best for the technique. Just a warning, the math in this book is highly complex. It requires a lot of work to go through this book, but the effort will be well rewarded.
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Reviewed in the United States on March 15, 2017
A
Verified Purchase
Adam
Phoenix, US
★★★★★ 4
Too Dry.
Format: Hardcover
This was a required textbook for my class in college. I think it was too dry. The book titled Deep Learning: From Curiosity To Mastery is much more approachable.
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Reviewed in the United States on May 22, 2026
A
Verified Purchase
Amazon Customer
Alexandria, US
★★★★★ 5
Comprehensive! The Bible of Deep Learning!
This book has by far surpassed my expectations! I have purchased many machine learning and deep neural network books in the past, but nothing has ever come close to this book! First of all, it is written by the fathers of Deep Learning, and is therefore an authority. Secondly, the book is broken into three parts: 1. A math overview and refresher. 2. Deep Learning applications and 3. Research in Deep Learning. I can't help but go through this book from front to back. It is a smooth read, and every sentence written is meaningful. These guys know their stuff! And after you read this book, YOU WILL ALSO know your stuff! If you feel daunted by the price, just remember, you get what you pay for! I'd say they could easily charge about $300+ for this book, but they are doing everyone a very kind favor by ONLY charging this reasonable amount. You get A LOT of bang for your buck with this purchase. I hesitated at first about buying this book because of the price, but I am soooooo happy that I did! Worth every penny! Look no further, get this book and start your Deep Learning journey!!
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Reviewed in the United States on July 14, 2017
M
Verified Purchase
mackster
Whiting, US
★★★★★ 1
A rushed, poorly written guide of how the "experts" can't really explain what Deep Learning is
Format: Hardcover
This book, in every sense of the word, is rushed. I think the authors wanted to establish themselves as leaders of this young-ish field, but does so by sacrificing quality. It also shows that Deep Learning theory has been there for a long time, known by another name called Neural Networks. The interesting algorithms are of MLP, Back Propagation and the classical neural networks. The optimization methods such as Adam are the ones that are new and interesting, and the only ones worthy of in this book. So, essentially, what you get from this book is use A for X, B for Y and C for Z type of dry, un-intuitive, badly written waste of paper. As for the structure of the book, it's like an example of how not to structure a book. It has some linear algebra, probability at the start (not good enough, and confuses more people and wastes paper). Goes on to prove other algorithms such as PCA (yeah, ok!). Then, talks about how this architecture works for this and that architecture. So, yeah, if you really want to try out deep learning, don't buy this book. Set up Tensorflow/pytorch/ other library, run the tutorials, find an architecture for the problem you are interested in and start tweaking that. You will have far more fun and would have saved your money. The praise that this book gets is beyond me. Did Musk even read this book? I doubt it.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on May 15, 2018

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