SKU: 14822203666

Makita 1.5 Ah 18V LXT Lithium‑Ion Compact Cordless 1/4 in. Impact Driver Kit (XDT11SY)

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Description

Makita 1.5 Ah 18V LXT Lithium‑Ion Compact Cordless 1/4 in. Impact Driver Kit (XDT11SY)18V LXT 1 4 in. Compact Impact Driver Kit Compact, high torque impact driver kit powered by Makitas 18V LXT LithiumIon system. Includes two 1. 5Ah batteries, charger, and tool bag for fast charging and all day productivity. Variable speed and dual LED lights make fastening tasks easier in tight or dark spaces. Key Features: Powerful Torque: Makita built 4pole motor delivers up to 1,460 inlbs for tough fastening jobs Variable Speed Control: 02,900 RPM

18V LXT 1/4 in. Compact Impact Driver Kit

Compact, high-torque impact driver kit powered by Makita’s 18V LXT Lithium‑Ion system. Includes two 1.5Ah batteries, charger, and tool bag for fast charging and all-day productivity. Variable speed and dual LED lights make fastening tasks easier in tight or dark spaces.


Key Features:

  • Powerful Torque: Makita-built 4‑pole motor delivers up to 1,460 in‑lbs for tough fastening jobs
  • Variable Speed Control: 0–2,900 RPM and 0–3,500 IPM for precision across materials
  • Compact & Lightweight: Only 5-3/8 in. long; 2.8 lb with battery reduces fatigue
  • Durable Build: All‑metal gear housing and XPT for improved dust and water resistance
  • Quick Bit Changes: 1‑touch 1/4 in. hex chuck with dual LED work lights with afterglow

Please check the Product Data Sheet Here


Specifications Table:

Specification Details
Battery Amp Hours 1.5 Ah
Battery Power Type Lithium-Ion
Battery Voltage 18V
Battery/Charger 2 Batteries & Charger Included
Blows per Minute (IPM) 3,500
Maximum Speed (RPM) 2,900 RPM
Motor Type Brushed
Drive Size 1/4 in. Hex
Included Batteries, Charger, Tool Bag
Power Tool Features Keyless Chuck, LED Light, Variable Speed
Torque 1,460 in‑lbs
Product Weight 10.3 lb (kit)
Product Depth 14.96 in
Product Height 9.17 in
Product Width 6.22 in
Certifications and Listings UL Listed
Color Teal
Cordless/Corded Cordless
Tool Length 5-3/8 in
Tool Weight with Battery 2.8 lb
LED Lights Dual with afterglow
Chuck Type 1‑touch 1/4 in. hex chuck
Protection Technology Extreme Protection Technology (XPT)
Computer Controls Star Protection Computer Controls

Frequently Asked Questions (FAQ):

Q: What batteries are included and are they compatible with other Makita tools?
A: The kit includes two 18V LXT compact 1.5Ah batteries (BL1815N), compatible with Makita 18V LXT tools with the Star symbol.


Q: Does this kit come with a charger and bag?
A: Yes, it includes a charger and a tool bag.


Q: What is the maximum torque and speed?
A: Up to 1,460 in‑lbs of torque, 0–2,900 RPM and 0–3,500 IPM.


Q: Is the tool suitable for harsh job site conditions?
A: Yes, it features Extreme Protection Technology (XPT) for increased dust and water resistance.


Q: What warranty is provided?
A: 3‑year limited warranty on the tool, batteries, and charger.


Compact power that elevates every project

This impact driver brings sleek, purposeful design to the jobsite, with Makita’s signature teal and streamlined silhouette. The dual LEDs cast a crisp glow, turning tight corners into clear workspaces while the compact body slips into places a drill can’t. Style it in a neatly organized kit—bag, charger, and batteries at the ready—for a workstation that looks as polished as it performs. It’s a refined essential that makes everyday builds feel decidedly luxe.


Equip your toolkit with compact power—add the Makita XDT11SY Impact Driver Kit to your cart today.

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SKU: 14822203666

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4.4 ★★★★★
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Shannon
Carnegie, 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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William P Ross
Lake Worth, 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
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Adam
Lowell, 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
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Amazon Customer
Whiting, 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
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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.
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Reviewed in the United States on May 15, 2018

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