SKU: 19395889500
akku 14 ah kompatibel zu jeep trekking- und mountain e-bikes

akku 14 ah kompatibel zu jeep trekking- und mountain e-bikes 36V 14Ah/504Wh Akku für Jeep Trekking E-Bikes TLR 7010, TMR 7000,TLR 7– PowerSmart

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Description

akku 14 ah kompatibel zu jeep trekking- und mountain e-bikes 36V 14Ah/504Wh Akku für Jeep Trekking E-Bikes TLR 7010, TMR 7000,TLR 7– PowerSmart36V 14Ah 504Wh Akku fr Jeep Trekking E Bikes TLR 7010, TMR 7000 TLR 7021 TLR 7020 Jeep Trekking E Bike TLR 7010 Trekking E Bike TLR 7011, Trekking E Bike TLR 7020, Trekking E Bike TLR 7021, Trekking E Bike TLR 7030, Trekking E Bike TMR 7000, Trekking E Bike TMR 7030, Mountainbike E Bike MHFR 7100, Mountainbike E Bike MHR 7000, Mountainbike E Bike MHR 7002, FZBYS3104421061599, Jeep MHR 5000 Ampere Deluxe, Ampere Hilux, Ampere X Trail E Bike Belgocycle

36V 14Ah/504Wh Akku für Jeep Trekking E-Bikes TLR 7010, TMR 7000 TLR 7021 TLR 7020

Jeep
 Trekking E-Bike TLR 7010
Trekking E-Bike TLR 7011,
Trekking E-Bike TLR 7020,
Trekking E-Bike TLR 7021,
Trekking E-Bike TLR 7030,
Trekking E-Bike TMR 7000,
Trekking E-Bike TMR 7030,
Mountainbike E-Bike MHFR 7100,
Mountainbike E-Bike MHR 7000,
Mountainbike E-Bike MHR 7002,
FZBYS3104421061599,
Jeep MHR 5000

Ampere Deluxe, Ampere Hilux, Ampere X-Trail E-Bike
Belgocycle Traveller Modelle 2019
Crussis 2022: e-Atland 6.7, e-Guera 6.7
Econic One Cross Country
Ezego Commute INT Modelle
F.lli Schiano E-Wave 28 Zoll
Jeep Trekking E-Bike TLR 7010
ks-cycling ALU MTB PEDELEC 27,5" ADORE ENFORCE E-BIKE ROT
Llobe Mountain-E-Bike 27,5" RapidRide
Proxy-Zyklus Lugano
Telefunken Expedition XC940 Jahr 2022
Tretwerk Bronx 5.0 28 Zoll
Txed El Speed ​​Road M20
Urbanbiker SIDNEY
Wayscral Everyway E250 T44 grau
Westhill Classic Step Through Elektrofahrrad

Dieser hochwertige Akku ist baugleich mit dem Akku Nummer ZZ374xxxx - REENTION EEL-MINI 40.

Es besteht eine große Verwechslungsgefahr mit anderen Semi-Intube/In-Frame Akkus!
Bitte unbedingt die genauen Maße vergleichen!


Paket

  • 11,6 Ah 1 x Lithium-Ionen-Akku


Technische Daten

  • Kapazität: 11,6 Ah / 417,6 Wh
  • Spannung: 36V
  • Ladespannungsgrenze: 42,5 V
  • Design: Im Rahmen
  • BMS-Typ: Standard-BMS
  • Ladebuchse: DC 2.1
  • Abmessungen: 430 x 68 x 74 mm
  • Betriebstemperatur Ladung: 0~45 °C
  • Betriebstemperatur Entladung: -20~60 °C

Dieser Akku ist mit folgenden E-Bike-Modellen kompatibel

Ampere
Ampere Deluxe
Ampere Hilux
Ampere X-Trail E-Bike

Belgocycle
Traveller Modelle 2021,

CRUSSIS
2022: e-Atland 6,7, e-Guera 6,7,
2021: e-Atland 6.6, e-Guera 6.6,
2020: e-Atland 6.5, e-Guera 6.5,

Econic One
Cross Country, Urban, Komfort,
Smart Cross Country, Smart Urban, Smart Comfort

EZEGO
Ezego Commute INT-Modelle,
Ezego Trail Destroyer Modelle

F.lli Schiano
E-Wave 28 Zoll,
E-Voke 28 Zoll,
E-Fully 27,5 Zoll,
Braver 27,5 Zoll,
E-Mercury 29 Zoll,
E-Jupiter 27,5 Zoll,


ks-radfahren
ALU MTB PEDELEC 27,5'' ADORE ENFORCE E-BIKE ROT,
ALU MTB PEDELEC 29'' ADORE ENFORCE E-BIKE BLAU,
ALU MTB PEDELEC 27,5'' ADORE XPOSE E-BIKE MITTELMOTOR SCHWARZ-ROT,
ALU MTB PEDELEC 27,5'' ADORE XPOSE E-BIKE SCHWARZ,

Llobe
Mountainbike-E-Bike 27,5" RapidRide

Proxy-Zyklus
Lugano, Lugano Elite, Lugano Prime

SMS
Txed El Speed ​​Road M20

Telefunken
Expedition XC940 Baujahr 2022,
Expedition XC941 Baujahr 2022,
Aufsteiger M925 - Baujahr 2022,
Aufsteiger M922 - Baujahr 2022,
Aufsteiger M923 - Baujahr 2022,
Aufsteiger M935 - Baujahr 2022,

TRETWERK
Bronx 5.0 28 Zoll,
Seville 5.0 28 Zoll,
Commander Fully 27,5 Zoll,

Urbanbiker
SIDNEY

Wegescral
Everyway E250 T44 grau,
Everyway E250 T48 grau ,
Everyway E450 T48 blau ,
Everyway E350 T48 grau,

Dallingridge
Malvern
Coniston

Westhill
Westhill Classic Step Through Elektrofahrrad,
    Westhill Venture Hybrid-Elektrofahrrad,
    Westhill Ranger Hybrid Crossbar Elektrofahrrad

Verehren
E-MTB Enforce 27,5"
E-MTB Enforce 29"
E-MTB Xpose 27,5"

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SKU: 19395889500

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4.3 ★★★★★
Based on 17 reviews
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Product Reviews
P
Verified Purchase
Par
Bozeman, US
★★★★★ 5
Excellent book on ML
Format: Paperback
This is a great book on machine learning. Topics covered are extensive - from beginner level to advanced topics including math behind different algorithms. However, not "all" algorithms are covered. Please go through the table of contents. The first part - 11 chapters - covers machine learning concepts and second part covers advanced topics with Pytorch. There are lots of excellent code and they work!! The quality of the book I received is excellent. I have gone through all 742 pages, and it has held up very well!! I used Jupyter notebook to run all examples. I created a new notebook and copied and pasted the code and ran them. This approach worked very well for me. At the same time, I could experiment with my take on the code snippets and definitely added to my knowledge. Only issue I have is on the second part of the book discussing PyTorch: (1) Some packages are a bit older version: e.g., transformer 4.9.1 whereas current version is 4.48+. It took some tweaking/recoding to get the examples working. (2) There is not much discussion on why certain architecture was chosen - e.g., number of layers, is there a rule of thumb on how to improve performance by changing these parameters? Even with CUDA the code run for a long time. Therefore, experimenting with different values of parameters become too time consuming. (3) On the same note, if I can achieve test accuracy of 90%+ using logistic regression and almost the same (perhaps one or two percent better with PyTorch with IMDB movie review dataset and that two much faster why should I use PyTorch for this dataset? Obviously, PyTorch is for certain types of problems. Discussions can be included by not adding to the exhaustive (and apt) contents. Personally I was disappointed by lack of any example on time series. Must have for ML practitioner as a reference and guide.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on December 20, 2024
R
Verified Purchase
Richard Hackathorn
Birmingham, US
★★★★★ 5
Excellent Textbook for Hands-On Learning of ML
Format: Kindle
This textbook is for the serious life-long learners of machine learning. There are at least two ways to ‘consume’ this book. For the expert in ML, this is a textbook to study as a clear comprehensive ML overview and then to dive into sections of interest or ignorance. The concepts are grounded in code examples and are well cited (with links) to sources. Further, this textbook is appropriate if you are TensorFlow-centric and want to broaden into cutting-edge ML models/tools coded in PyTorch. For a new learner to ML, this is a textbook to DO (not just READ) with hands-on and brain-engaged. If you realize that ML is a key life-long skill for your career, consider this textbook as part of a daily learning habit (10-30 min). From personal experience, my advice to the new learner is as follows… First, clone the GitHub repository, setup your Python environment, and study the textbook, while working through the notebooks. Go on tangents and break the code. Do this methodically as part of your daily learning habit, but do not hesitate to jump ahead several chapters to prepare for tomorrow’s meeting. There is enough excellent material here for a full year of ML adventures. I did a similar strategy with Raschka’s first textbook. About four years ago, I had finished Andrew Ng’s Deep Learning Specialization as a student in his first cohort. I knew the concepts well but could not do the actual application coding. I was surprised how my Python coding improved by following Raschka’s clean and elegant style. And Raschka’s code examples were meaty enough to be springboards into working applications. Several textbook editions later, what is different about this new edition? First, it moves you through scikit-Learn (a firm foundation) to PyTorch, instead of TensorFlow. PyTorch is a better stepping-stone, both conceptually and practically. With PyTorch, you will go further with less energy, while being able to convert your efforts into TensorFlow as needed. In addition, most of the cutting-edge ML/AI/DL research is in PyTorch. It is nice to read a recent arXiv paper, clone their repository, click on the Colab tutorial, and replicate their experiments, along with picking up a ton of new coding tricks & tips. I am excited to work through these PyTorch sections to hone my skills. Second, there is a clear recognition of model tracking and tuning practices. This is often a gap in other ML textbooks and courses. Once you progress beyond the simple demo examples in a lecture, you realize that the real work is experiments, more experiments, and still more experiments, so that you must understand what the model architecture and hyperparameters are doing to your dataset. There is good coverage of scikit-Learn pipeline, grid search, model performance, and the like. Third, ML/AI/DL practice is rapidly evolving. Every week new ML packages/services become available that could save much grief on your current project. What is refreshing about Raschka’s textbook series is that he constantly adding cutting-edge topics because he likes to stay current and to help us stay current. Hence, this edition contains recent ML treats as: transformers, self-supervised learning, autoencoders-to-GAN, graph neural networks, DBSCAN, t-SNE (with brief mention of UMAP), and PyTorch-Lightning.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on February 26, 2022
A
Verified Purchase
Amazon Customer
Charlottesville, 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
Belleville, 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
Waukegan, 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

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