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QA1 Stocker Star Series Rear Shock Absorber - Single Adj. - 11in/15.875in - AluminumQA1 offers stock mount aluminum adjustable shocks for 64 73 Mustangs as well as several other Ford and Mercury applications. These applications include Fairlanes, Falcons, Torinos, Comets, Cougars and cyclones. Available in double, single and non adjustable valving allows, you are sure to get exactly the ride you want. With these shocks you'll get great looks and high performance handling all at a great price. These lightweight billet aluminum smooth
QA1 offers stock mount aluminum adjustable shocks for 64-73 Mustangs as well as several other Ford and Mercury applications. These applications include Fairlanes, Falcons, Torinos, Comets, Cougars and cyclones. Available in double, single and non-adjustable valving allows, you are sure to get exactly the ride you want. With these shocks you'll get great looks and high performance handling all at a great price. These lightweight billet aluminum smooth body shocks are made in the USA and offer easy, bolt-in installation. They are 100% dyno tested and serialized and serviceable and rebuildable by QA1 authorized service centers.This Part Fits:
| Year | Make | Model | Submodel |
|---|---|---|---|
| 1966-1970 | Ford | Fairlane | 500 |
| 1966-1967 | Ford | Fairlane | 500XL |
| 1966-1969 | Ford | Fairlane | Base |
| 1960-1970 | Ford | Falcon | Base |
| 1962 | Ford | Falcon | Deluxe |
| 1963-1970 | Ford | Falcon | Futura |
| 1963-1965 | Ford | Falcon | Futura Sprint |
| 1964-1973 | Ford | Mustang | Base |
| 1969-1971 | Ford | Mustang | Boss 302 |
| 1971-1972 | Ford | Mustang | Boss 351 |
| 1969-1970 | Ford | Mustang | Boss 429 |
| 1970-1973 | Ford | Mustang | Grande |
| 1970-1973 | Ford | Mustang | Mach 1 |
| 1965-1970 | Ford | Mustang | Shelby GT-350 |
| 1966 | Ford | Mustang | Shelby GT-350H |
| 1967-1970 | Ford | Mustang | Shelby GT-500 |
| 1968 | Ford | Mustang | Shelby GT-500KR |
| 1971 | Ford | Torino | 500 |
| 1968-1971 | Ford | Torino | Base |
| 1970-1971 | Ford | Torino | Brougham |
| 1969-1971 | Ford | Torino | Cobra |
| 1968-1971 | Ford | Torino | GT |
| 1969-1971 | Ford | Torino | Squire |
| 1970-1971 | Ford | Torino | Super Cobra Jet |
| 1964-1965 | Mercury | Comet | 202 |
| 1964-1965 | Mercury | Comet | 404 |
| 1960-1969 | Mercury | Comet | Base |
| 1962-1963 | Mercury | Comet | Custom |
| 1961-1963 | Mercury | Comet | S-22 |
| 1967-1973 | Mercury | Cougar | Base |
| 1969-1970 | Mercury | Cougar | Boss 302 |
| 1969-1970 | Mercury | Cougar | Boss 429 |
| 1969-1970 | Mercury | Cougar | Cobra Jet |
| 1967-1973 | Mercury | Cougar | XR-7 |
| 1968-1971 | Mercury | Cyclone | Base |
| 1969 | Mercury | Cyclone | CJ |
| 1970-1971 | Mercury | Cyclone | GT |
| 1969-1971 | Mercury | Cyclone | Spoiler |
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4.3 ★★★★★
Based on 22 reviews
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Product Reviews
★★★★★ 3
A Broad Overview, But Light on Modern Fine-Tuning
Format: Paperback
I'm currently really interested in fine-tuning LLMs and recently completed my first LoRA-based fine-tuning on a quantized model. I came to this book looking for more detail on fine-tuning. While it touches on the topic, I found the content didn’t quite align with the current state of the field in 2025. Techniques like LoRA, QLoRA, and PEFT weren’t really covered, and the material leaned more toward what I think are older or lower level approaches. That made it harder to connect with what I’m actually working on.
That said, when I shifted to other chapters — like the sections on prompt engineering techniques such as Chain of Thought (CoT) and Tree of Thought (ToT) — I found more value. These sections were clearer, and I picked up a few practical insights, like using few-shot examples that walk through the CoT reasoning process. That’s not something I’ve tried before, and I can see how it might help smaller models that struggle with any type of reasoning tasks.
Overall, the book feels more like a broad overview of all LLM concepts. For someone exploring many topics across the LLM ecosystem, it offers a wide-ranging introduction. But for readers like me who are actively trying to learn and apply techniques like fine-tuning and quantization, it may leave you wanting up-to-date guidance.
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Reviewed in the United States on August 10, 2025
★★★★★ 5
Great foundation read for security!
Format: Paperback
This book is a great read! It builds a strong foundation and I would highly recommend it for builders who are interetsed in building on LLMs and ensuring everything is secure. Security is super important and this book does it justice!
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Reviewed in the United States on June 27, 2025
★★★★★ 5
Loved it
Format: Paperback
I’ve easily read dozens of tech books. I liked this one a lot. Sure, there were boring parts, but most of it was engaging, especially on dry subjects. I previously read “How AI Works” and found this more informative and way more enjoyable. I got through the 700 pages in about 5 weeks while also learning about probability and linear algebra from other books and online sources. I’d love to read something more advanced by the author, maybe getting into more modern applications. I feel more comfortable with the subject and feel I am now ready to conquer more advanced texts. I initially picked this up to give me some background before reading “How to Build a LLM (from scratch)”. I’ve ordered an intermediary Deep Learning with Python book as well, but wouldn’t mind a more advanced theory book to accompany these books. I’ll definitely be rereading sections of this book to further familiarize myself with topics like backpropagation. Highly recommend if you’re looking for a gentle, but broad introduction to the topic.
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Reviewed in the United States on November 14, 2025
★★★★★ 5
A Good Place to Start Learning AI
Format: Paperback
Diving into the world of artificial intelligence can feel like stepping into a vast, uncharted ocean, and if you're looking for a reliable vessel to navigate these waters, this book is an excellent choice. However, I must be candid—this journey is not for the faint-hearted or those hoping to breeze through. The subject of AI, with its complex algorithms and intricate theories, is notoriously challenging. You won't find yourself flipping pages at a rapid pace, as this is not a title designed for speed-reading. Instead, it demands your full attention and a willingness to engage deeply with the material.
At the heart of AI lies mathematics—a fundamental pillar that underpins the entire discipline. This book, while comprehensive, offers only a glimpse into the mathematical framework that drives artificial intelligence. But don’t be disheartened by this. Think of it as a solid foundation, a primer that will arm you with the essential concepts needed before you delve deeper into the more advanced mathematical intricacies elsewhere. When you do eventually tackle those more complex equations, you'll find yourself better equipped, with a clearer understanding of the principles at play.
I should also mention that I'm no stranger to Andrew's work. Having explored some of his other writings, I can confidently say that he possesses a unique flair for communication. His ability to distill complex ideas into accessible language, without losing the essence of the subject, is truly commendable. Andrew writes with a certain finesse and sophistication that makes even the most daunting topics seem approachable. His style is not just informative, but also engaging, with a touch of elegance that sets his work apart from others in the field.
In summary, while the path to mastering AI is undeniably steep, this book serves as an invaluable guide. It’s not just a starting point; it’s a beacon for those who are serious about understanding the intricacies of artificial intelligence. Be prepared to invest time and effort, and in return, you'll gain a solid foothold in a subject that is as fascinating as it is complex.
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Reviewed in the United States on September 2, 2024
★★★★★ 5
An enjoyable, and seriously excellent, path to understanding Deep Learning...
Format: Paperback
Deep Learning is changing our world. If you want to understand more, this is a great place to start. Andrew Glassner is a talented explainer - I took his short course on Deep Learning and learned so much, but also came away impressed at how well he can make complex material so clear and engaging. And this book is jammed packed with insights, visuals, and clear explanations. The author has a playful, sometimes quirky style that shines through, which gives this tour a lot of personality as well as information. Very enjoyable reading - I felt like he captured all that was good about his course (and then some) and bottled it up in this book. There is a lot more material here than in that course, and it is well laid-out and organized so that it is easy to roam around and come back to review the pieces that matter to you.
Even if you plan to go to on to be a world-class Deep Learning engineer or mathematician, you have to start by understanding the concepts. And this book does a great job of presenting all the core ideas in a way that makes them clear and memorable.
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Reviewed in the United States on August 5, 2021
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