SKU: 30839798326

Premium Battery for Asus PadFone A80, Infinity A80, Padfone infinity 3.8V, 2300mAh - 8.74Wh

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Description

Premium Battery for Asus PadFone A80, Infinity A80, Padfone infinity 3.8V, 2300mAh - 8.74WhOverview Categories Mobile SmartPhone Battery Compatibles # View Full List of OEM numbers and models here Brand Cameron Sino Voltage 3. 8V Chemistry Li Polymer Capacity 2300mAh Dimensions 93. 61 x 40. 76 x 4. 14 mm Weight 90. 0g Bar Code 4894128085379 Type Brand New Original Battery Warranty 1 Year Performance Guaranteed to Meet or Exceed Original Performance Certifications ISO9001, RoHS, and CE * Please make sure that the device manufacturer, The

Overview
Categories
Mobile/SmartPhone Battery
Compatibles #
View Full List of OEM numbers and models here
Brand
Cameron Sino
Voltage
3.8V
Chemistry
Li-Polymer
Capacity
2300mAh
Dimensions
93.61 x 40.76 x 4.14 mm
Weight
90.0g
Bar Code
4894128085379
Type
Brand New Original Battery
Warranty
1 Year
Performance
Guaranteed to Meet or Exceed Original Performance
Certifications
ISO9001, RoHS, and CE

* Please make sure that the device manufacturer, The model number and also the battery part number are all listed in our product details. Some manufacturers have more than one battery type fitted in like models. We recommend that you remove the original battery before ordering to confirm this. If you are not sure we may be able to confirm for you.

* Please be advised that some higher capacity batteries are larger than the standard battery from the manufacturers. Please make sure that your model number is shown in the information above before ordering. If there is any doubt contact us first and we can advise if this is a suitable replacement for your device.

Description

Mobile/SmartPhone Battery Articles & Tips

iTEKcanada Offers Free Shipping to All of Canada. ( Free Shipping for All Order over 59$+ ) !
The CS-AUP800SL Mobile/SmartPhone Battery will solve your battery problems or provide you with a spare battery. The CS-AUP800SL Mobile/SmartPhone Battery comes with a 30 day money back guarantee and 1 Year Warranty. Once we have received your new order for the CS-AUP800SL Mobile/SmartPhone Battery, it will be processed within 24-48 hours or on the next business day via CanadaPost Mail.

Purchasing Mobile/SmartPhone Battery
Mobile/SmartPhone Battery by iTEKcanada.com are usually lower in price and usually exceed the performance of OEM Mobile/SmartPhone Battery. Many of iTEKcanada.com’s Mobile/SmartPhone Battery use the same battery cells as OEM batteries.

What is mAh / Wh?
These ratings show battery`s capacity. Think of them as gas in your car tank, the bigger the tank, the more fuel it will carry and your car will drive longer. Same with these batteries, the higher the mAh/Wh, the longer your device will run between recharges.

Will your higher capacity (mAh/Wh) Mobile/SmartPhone Battery work in my device, even though my original battery has a smaller rating?
Definitely! Our batteries are designed to be 100% replacements for original batteries. Higher mAh/Wh rating means longer run time between recharges. The higher the rating the slower it will take for the batteries to drain down. Meaning you will get longer talk time with a higher mAh rating. In other word, higher is the mAh number, better it’ll be for your device.

Quality and Safety
This battery contains advanced technical components and has been tested according to strict CE safety standards. The battery contains a chip that prevents overcharging and short circuiting. Next to this the battery is made from high quality cells that do not suffer from a so-called 'memory effect'.'

Factory Fresh
Batteries lose performance over their lifetime, even when they are not used. Many importers and resellers store their batteries in stock for a long time before they are sold. iTEKcanada.com sends products almost directly from the factory, so you are sure that you will get a brand new high quality battery!

Compatible Devices & Replacements

Compatible Batteries

Device Manufacturer
Battery Number
Asus
C11-A80

Compatible Devices

Device
Manufacturer
Model
Mobile/SmartPhone Battery
Asus
A80
Mobile/SmartPhone Battery
Asus
A80C
Mobile/SmartPhone Battery
Asus
A86
Mobile/SmartPhone Battery
Asus
Infinity A80
Mobile/SmartPhone Battery
Asus
PadFone A80
Mobile/SmartPhone Battery
Asus
Padfone infinity
Mobile/SmartPhone Battery
Asus
Padfone infinity Lite
Mobile/SmartPhone Battery
Asus
T003
Mobile/SmartPhone Battery
Asus
T004
Shipping Notes
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SKU: 30839798326

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4.7 ★★★★★
Based on 24 reviews
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Product Reviews
A
Verified Purchase
Amazon Customer
Houston, US
★★★★★ 4
Just learning it
Format: Paperback
Nice learning book just have to finish it
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on December 10, 2025
K
Verified Purchase
Kindle Customer
New York, US
★★★★★ 5
Very useful book
Format: Paperback
I use it for the machine learning class I teach.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on May 3, 2026
T
Verified Purchase
Tommy Jonsson
Lexington, US
★★★★★ 5
Cover many areas in detail and recommendations for more to read for what's outside
Format: Paperback
Good book!
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on May 4, 2026
M
Verified Purchase
Moses Kayanda
Massapequa, US
★★★★★ 5
One of the best machine learning books...
Format: Paperback, Format: Paperback
Machine Learning can often be intimidating whether you are starting out or already a practitioner. It is easy to get stuck on one concept, walk away frustrated, or just copy that code you find on StackOverflow without really understanding what it does. What the authors of this book, Machine Learning with PyTorch and Scikit-Learn, have managed to do is to keep the reader engaged giving a deeper illustration as to how the concepts work. In this book, you get practical code examples, a detailed explanation of how the various library tools work, and exposure to the mathematical concepts behind machine learning algorithms. In addition, what I like about the book unlike many machine learning books is that the authors have managed to intuitively explain how each algorithm works, how to use them, and the mistake you need to avoid. I have not read a Machine Learning book that better explains Transformers as this one does. The authors have managed to give a detailed dive into this model architecture through well-explained codes and illustrations. As a reader, you walk away having intuitively grasped the concepts of attention and self-attention in ways that will make this crucial NLP architecture clear. You get exposed to pre-trained models from HuggingFace library which really helps to have that hands-on experience working with large datasets. As they have done throughout the book, the authors have broken down those complex mathematical operations into simple explanations that are easy to follow. What I generally like about the book is how it seamlessly connects all the chapters, not throwing off the reader. There are numerous external resources quoted throughout the book. This helps spark that curiosity to dig deeper. In addition, you get introduced to PyTorch, getting exposed to all those sophisticated libraries that help the reader learn how to maximize their compute power. I would say it is not intimidating at all even if you have not used PyTorch before. I would recommend this book to anybody seeking a textbook that is both easy to read and modern in its content. If were to rate the book I will give it a 10/10 as it really applies to both beginners and experienced practitioners, covers all the concepts one needs to apply in their operations, and acts as a quick reference.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on March 1, 2022
G
Verified Purchase
Gabe Rigall
Carnegie, US
★★★★★ 5
Thorough Primer for Machine Learning and PyTorch
Format: Paperback
BLUF: A thorough primer for machine learning enthusiasts with plenty of theory to underscore its many practical examples. A definite must-have for anyone looking to add PyTorch to their machine learning tool belt. PROS: - Extremely thorough (if not comprehensive). I really appreciate that this book doesn't just thrust one into building models with PyTorch. It starts at the "beginning" and provides examples, theory, additional resources, and citations along the way. - Theory. Those whose calculus and linear algebra courses ended many years ago will appreciate (if not remember exactly) the mathematical theory and notation that accompanies almost every paragraph. This book gives one the opportunity to "dig deeper" or stay in the shallows until the notation stops. - Python. Rather than simply utilizing Scikit-Learn to illustrate concepts and introduce models, this book contains many sections where models (such as a Perceptron) are coded from the ground up so the reader can fully understand the underlying mechanics. Python enthusiasts will nerd out. Parents of small children might want to skip a few pages. - Graphs, charts, and graphics. There are plenty of places where a drier text might have foregone the use of graphs. This text does not. It does however refrain from overusing them. - PyTorch. This should be obvious from the title, but this text prioritizes PyTorch instead of TensorFlow. This is especially helpful for those looking for an alternative to Keras and TensorFlow as the PyTorch API is very user-friendly. CONS: - Almost too much code. This isn't a true "con" but anyone wanting to emulate or follow along with the examples would do well to get the digital edition so they can copy and paste. - Length and complexity. Anyone hoping for a "quick read" or a "quick start guide" will be disappointed. This book hovers somewhere between an undergraduate primer and a graduate-level text for length and readability. This is not to say that it's difficult to read, merely that there are other "quick start" / "practical" texts out there that cater more to a lay audience.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on February 26, 2022

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