SKU: 60839707574

Radium Engineering 13-Up Focus ST / 16-18 RS / 15-Up Mustang Eco PCV Baffle Plate OEM Configuration

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Radium Engineering 13-Up Focus ST / 16-18 RS / 15-Up Mustang Eco PCV Baffle Plate OEM ConfigurationRadium Engineering 13 Up Focus ST 16 18 RS 15 Up Mustang Eco PCV Baffle Plate OEM Configuration This Part Fits: Year Make Model Submodel 2016 2017 Ford Focus RS 2013 2017 Ford Focus ST 2017 Ford Fusion Energi Platinum 2013 2017 Ford Fusion Energi SE 2013 2017 Ford Fusion Energi Titanium 2010 2012 Ford Fusion Hybrid 2017 Ford Fusion Platinum 2017 Ford Fusion Platinum Hybrid 2010 2017 Ford Fusion S 2014 2017 Ford Fusion S Hybrid 2010 2017 Ford Fusion SE

Radium Engineering 13-Up Focus ST / 16-18 RS / 15-Up Mustang Eco PCV Baffle Plate OEM Configuration

This Part Fits:

Year Make Model Submodel
2016-2017 Ford Focus RS
2013-2017 Ford Focus ST
2017 Ford Fusion Energi Platinum
2013-2017 Ford Fusion Energi SE
2013-2017 Ford Fusion Energi Titanium
2010-2012 Ford Fusion Hybrid
2017 Ford Fusion Platinum
2017 Ford Fusion Platinum Hybrid
2010-2017 Ford Fusion S
2014-2017 Ford Fusion S Hybrid
2010-2017 Ford Fusion SE
2013-2017 Ford Fusion SE Hybrid
2010-2012 Ford Fusion SEL
2010-2012,2017 Ford Fusion Sport
2013-2017 Ford Fusion Titanium
2013-2017 Ford Fusion Titanium Hybrid
2015-2017 Ford Mustang EcoBoost
2015-2017 Ford Mustang EcoBoost Premium
2007-2013 Mazda 3 Mazdaspeed
2006-2007 Mazda 6 Mazdaspeed
2005 Mazda Miata Base
2005 Mazda Miata LS
2005 Mazda Miata Mazdaspeed
2015 Mazda MX-5 Miata 25th Anniversary Edition
2006 Mazda MX-5 Miata Base
2013-2015 Mazda MX-5 Miata Club
2006 Mazda MX-5 Miata Club Spec
2006-2015 Mazda MX-5 Miata Grand Touring
2011-2012 Mazda MX-5 Miata Special Edition
2006-2015 Mazda MX-5 Miata Sport
2007-2009 Mazda MX-5 Miata SV
2006-2012 Mazda MX-5 Miata Touring
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SKU: 60839707574

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4.2 ★★★★★
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noam barkay
Port Orchard, US
★★★★★ 5
Excellent book to truly understand LLM design patterns
Format: Paperback
I just finished reviewing Ken Huang's pocket book on LLM Design Patterns, and WOW what an amazing resource! This book is excellent if you want to truly understand how to create and enhance intelligent AI language models, all that in your pocket! Ken makes the difficult things seem surprisingly easy, and that's the real MAGIC. - How to prepare your data for training by making it extremely clean. Developing the brains: the practical aspects of training, optimizing, and maintaining your models. - Learn amazing prompting techniques (such as Chain-of-Thought and Tree-of-Thoughts) to improve your AI's reasoning and problem-solving abilities. Learn everything there is to know about RAGs so that your LLM can incorporate outside expertise. - It also delves into creating "agentic" AI that is capable of action and planning (not only simple plan and execute but also enhanced techniques like ReWoo!) Really, this feels like a useful toolkit, so Ken thank you for that resource Thanks, Idan Habler
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on June 9, 2025
R
Ryan Meyer
Battle Creek, US
★★★★★ 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
V
Vineeth Sai
Grantham, US
★★★★★ 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
C
Verified Purchase
CL
Belleville, US
★★★★★ 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
A
Verified Purchase
Amazon Customer
Cuba, US
★★★★★ 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

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