SKU: 93414913736

Foldable KOOLUX X11 750W

$567.00 $630.00
Save 10%
No.2 Bestseller on salfueradeti.es >

Pay in installments of $157.50 with ShopPay, AfterPay and Klarna

Shipping Estimate
USA
  • USA
  • CAN

Ships within 48 hours · Estimated delivery Aug 4 - Aug 9

Promo Codes Available:

For Your Every Summer RSVP, with Code: SUMMER15

Description

Foldable KOOLUX X11 750WThe KOOLUX X11 is a powerful electric bike with wide tires and a dual battery system, which provides exceptional range and high stability on various surfaces. Equipped with 750W motor and two 48V 13 + 10. 4Ah batteries, results in a total capacity of 23. 4Ah, providing an impressive range of up to 150 km can be achieved. With a maximum speed of 50 km h The KOOLUX X11 offers fast and efficient movement. The 4. 0 inch fat tires provide excellent


The KOOLUX X11 is a powerful electric bike with wide tires and a dual-battery system, which provides exceptional range and high stability on various surfaces. Equipped with 750W motor and two 48V 13 + 10.4Ah batteries, results in a total capacity of 23.4Ah, providing an impressive range of up to 150 km can be achieved.

 

 

With a maximum speed of 50 km/h The KOOLUX X11 offers fast and efficient movement. The 4.0-inch fat tires provide excellent traction and stability on asphalt, gravel or sand. Thanks to the foldable aluminium frame The e-bike can be easily transported and stored in a space-saving manner.

The KOOLUX X11 is the ideal choice for anyone looking for a powerful, environmentally friendly and versatile electric bike – perfect for everyday use and adventurous rides on different routes.

You can choose between 3 driving modes: pedal assist mode, pure electric mode (if you use the throttle grip included as an accessory) and classic pedal mode. In normal pedal mode, you can experience unique acceleration with the 7-speed Shimano gears and mechanical gears. In moped mode and pure electric mode, you can enjoy an unexpectedly fast driving experience.

 

 

The KOOLUX X11 is made of aluminium alloy, which significantly reduces the weight of the bike while remaining strong and durable. With a total weight of only 38.6 kg and a load capacity of 150 kg, it is easy to transport. The foldable design allows for space-saving storage - when folded, it measures just 90 cm x 54 cm x 74 cm, so it can be easily stored in the trunk and is ready to use anytime, anywhere.

 

Wider 4.0-inch tires for more stability - KENDA

The KOOLUX X11's 4.0-inch tires provide a larger contact patch with the ground compared to standard tires, providing improved balance and stability. Ride with confidence whether on city streets or rough terrain.

 

 

Versatile 3-level pedal assistance mode

The 3-level pedal assist mode (PAS) allows you to choose between speeds of 15 km/h, 20 km/h and 25 km/h, so you can tailor your ride to your personal preferences. With its stylish design, this e-bike is ideal for commuting and various riding situations - ensuring that you travel not only comfortably but also elegantly.

Comfortable driving experience – on and off the road

You don't need to buy two e-bikes to ride both in the city and outside. With the KOOLUX X11 you can experience both safely, reliably and with lots of fun. Not only does it offer a powerful and efficient drive, but it also has all the features you need for every ride.

 


Dual hydraulic disc brakes

The KOOLUX X11 is equipped with dual hydraulic disc brakes that provide exceptional braking performance and maximum control. Thanks to the precise responsiveness and powerful deceleration, you can stop safely in any situation, whether on wet roads or at high speed. This advanced braking technology ensures a reliable and safe riding experience.

 

Model X11
Engine position Rear hub motor
Battery position Rear luggage rack
Corridors 7 gears
Battery capacity 23.4 Ah / 48V
Range per charge 150KM
Tire size 20" x 4.0″
Frame material Aluminium alloy
Load capacity 150kg
Wheel size 20″
Start mode Turn the switch
Maximum speed 50km/h
Weight 36.5kg
Tension 48V
Maximum gradient 25 degrees
Power supply 18650 lithium-ion battery
Size 175cm x 60cm x 131cm
Water resistance IP54
Braking system Hydraulic brakes
Operating temperature -10~45 degrees
Drive mode Manual support
Engine type Powerful brushless motor

 

Shipping Notes
  • Free Standard Shipping on $100+ Orders to the USA.
  • Except Preorder products are shipped in 48 hours.
  • Delivery to the USA:
  1. Standard Shipping : 3-10 business days
  • If time is of the essence, please consider selecting expedited delivery for faster service.
Exchange/Return Notes
  • We offer a 30-day return/exchange service after receiving.
  • Final sale items are not eligible for returns or exchanges.
  • To process your return/exchange, please contact us at [email protected]
  • Please click here for more details>>> Return & Exchange Policy
SKU: 93414913736

Discover Niche Categories That Outsell

Top-Converting Item to Boost Your Average Order

4.5 ★★★★★
Based on 6 reviews
Sort
Highest Rating
Newest First
Oldest First
Product Reviews
X
Verified Purchase
0x00000000:00000000
Chelsea, US
★★★★★ 5
Excellent book, possibly currently unique in coverage of latest ideas
This book is possibly currently unique in its coverage of the latest ideas in the field of deep learning -- and it is a very convenient and good survey of fundamental concepts (linear algebra, optimization, performance metrics, activation function types), different network types (multi-layer perceptron, convolutional neural networks, and recurrent neural networks), practical considerations (data set, training and validation, implementation), and applications (comments on existing real-world/commercial uses). The final 235 pages of the content portion of the book is dedicated to topics in "Deep Learning Research", and these topics are truly at the current frontier. Another reviewer said that one could gain the same knowledge of cutting-edge research by reading all of the latest papers (from academia and industry), but the "research" section of this book offers the following: Selection of the most notable research by the very experienced authors of the book, and collection of similar research in to a broader discussion of themes, and the additional insights. The book covers very advanced and new ideas currently being explored, and it is very nice to be able to have a consistent and coherent presentation of all of those ideas. However, the book is also packed with valuable observations and pointers about more basic aspects of deep learning implementations and practices -- and such commentary is in depth and includes substantial analysis and mathematical derivation (in an intuitive presentation that often includes graphs illustrating the phenomenon). As someone with an intermediate level of knowledge and experience of neural networks, I am really grateful for this book, because seems like the ideal resource for learning cutting-edge ideas and practices, with context. The book has excellent scope and depth, and I am confident that anyone with a solid background in linear algebra, calculus, statistics, and general machine learning, and basic neural networks (multi-layer perceptrons) will find this book to be very exciting and perhaps unique in its ability to take the reader to the next level and a new frontier. I was personally excited to learn about the idea of representing the dependencies of intermediate quantities by directed graphs, and how this can be used to perform calculations for recurrent neural networks efficiently. And I think the long chapter on recurrent neural networks is very helpful. Having said all of this, I think only people with significant working knowledge and experience with neural networks and mathematics -- people whose academic or professional focus has been neural networks for at least a year or two -- would benefit from this book. This book answers a lot of the deeper questions that one is likely to have while developing a solid understanding of the fundamentals, and that's one of the book's tremendous values, but this book assumes an understanding of the fundamentals (but does briskly cover the basics). I think this book is a perfect follow-up book for the excellent book "Neural Network Design (2nd edition)" by Hagan, Demuth, Beale, and de Jesus, and I highly recommend the latter for gaining the solid background needed to have a thrilling experience with the "Deep Learning" book. In summary, I am very glad this "Deep Learning" book was written, and I think the "Deep Learning" book will be a great benefit to a lot of people, and to the evolution of the field.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on April 18, 2017
Z
Verified Purchase
Zygerian99
Draper, US
★★★★★ 5
The definitive guide to becoming a researcher in the field
Format: Hardcover
This is not a coding book. I see a lot of negative reviews around the expectation that this book would teach the reader how to quickly build machine learning systems and write code. This book is not for that audience. If you just want to build applications, don't worry about how deep learning works. It's akin to needing to understand how an engine works just to drive a car. If you are looking for a coding resource, try: https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1492032646/ref=sr_1_4?keywords=machine+learning+tensorflow&qid=1579608765&sr=8-4 . And even with that book, the material still goes far beyond what you need - use it as a light reference. I bought this book as an aspiring machine learning researcher, and towards that end, it is the best resource available in print (still true as of 2020). For instance: The first 5 chapters are timeless. These are things that were mostly established 20 or 30 years ago and beyond and are mostly STEM fundamentals at this point. There are whole textbooks dedicated to each of those chapters, but the authors provide a quick refresher and overview of probably 80% of what you'll encounter in deep learning. If you haven't previously learned each of these subtopics, you'll probably want to study them individually since they are the key to innovating (linear algebra, probability & stats, numerical computation, machine learning fundamentals). Chapters 6 thru 9 are the foundation of deep learning. We're about 12 years into seeing rapid change in the deep learning space, yet all of these principles and techniques still hold (many recent innovations are still relying on Convolutional models in 2020, which is the most layered/complex topics in those chapters). Therefore, I'd wager that these chapters are also fairly stable knowledge that is worth internalizing if you want to be deeply involved in the future of machine learning. Chapters after 9 are mostly experimental topics, and many of them are already the wrong strategies for optimal results. But there are interesting ideas in here that you'll often encounter in the wild, so it's good exposure to various topics. But probably not worth much of your time. And lastly, there is good history in here from people who know the space intimately. It's a good way to piece together the developments and learn the lexicon of deep learning so you can have intelligent conversation with experts.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on January 21, 2020
S
Verified Purchase
Shannon
Omaha, 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!!
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on November 30, 2025
W
Verified Purchase
William P Ross
Draper, 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.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on March 15, 2017
A
Verified Purchase
Adam
New York, 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.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on May 22, 2026

recommand products

Ditt Inre: Vard Mer An Guld - VINYL LP
20.98
Diffuser Cat Relaxant Refill Plus Additional Bottle Calming Spray - Refills Compatible with All Major Brand diffusers. Comfort Zone, ThunderEase, Relaxivet and Others
36.39
Ultramarine: This Time Last Year - VINYL LP
19.98
BENCMATE Protective Inflatable Collar for Dogs and Cats - Soft Pet Recovery Collar Does Not Block Vision E-Collar (Small, Dark Grey)
20.99
Pet Honesty Cat Skin & Coat Health Chews - Omegas, Vitamin C + E, Biotin Supplement, Soothes Skin and Promotes Shiny Coat, Cat Supplements & Vitamins - Chicken (30-Day Supply)
20.99