SKU: 34092450321

MONDRAKER ZENDIT RR E-MTB

$4814.55 $5349.50
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Description

MONDRAKER ZENDIT RR E-MTBComponents Technical specs Frame Zendit Stealth Air full Carbon, Updated Zero Suspension System, 165mm travel, Forward Geometry, internal battery, Boost 12x148mm rear axle, UDH, tapered head tube, one piece Monoblock upper link, HHG internal cable routing, MAX capacity sealed bearings, oversize 15mm axles, exclusive plastic motor covers. Sizes S: 380mm M: 410mm ML: 435mm L: 460mm XL: 490mm Rear shock Fox Float X Factory Kashima Evol LV 205x65mm. 2

 

Components

Technical specs

  • Frame

    Zendit Stealth Air full Carbon, Updated Zero Suspension System, 165mm travel, Forward Geometry, internal battery, Boost 12x148mm rear axle, UDH, tapered head tube, one-piece Monoblock upper link, HHG internal cable routing, MAX capacity sealed bearings, oversize 15mm axles, exclusive plastic motor covers.

     

    Sizes

    S: 380mm / M: 410mm / ML: 435mm / L: 460mm / XL: 490mm

  • Rear shock

    Fox Float X Factory Kashima Evol LV 205x65mm. 2-positions lever compresion (Open/Firm), low-speed compression, low-speed rebound, air preload. Trunnion top mount, 30x8mm bottom bushings.

     

    Shock settings

    CS 30 compression, 1,1 Bleed, RL50 rebound, Rezi B40, LML, standard MCU, 0 volume spacers

  • Fork

    Fox 38 29 Float GRIP X2 Factory Kashima, 170mm, tapered steerer tube, Boost 15x110mm Kabolt axle, 44mm offset. High and low-speed compression, high and low-speed rebound, air preload.

     

    Headset

    Acros custom ZS56/ZS56, 1-1/8", 1.5" head tube, Internal cable routing, sealing plugs, without blocklock.

  • Stem

    Onoff S6 30mm 0º, 6061 forged alloy + CNC, 31.8mm barbore

     

    Handlebar

    Onoff S9 1.0 7050 Carbon, rise: 25mm, width: 800mm, 8º backsweep, 5º upsweep, 31.8mm barbore. L and XL size: 38mm rise.

  • Grips

    Onoff Desert Pro, CNC lock-on, 135mm. L and XL: Thicker size

     

    Seatpost

    Onoff Pija dropper internal, diameter 31.6mm, 1X Remote lever with bearing, S size: 140-115mm (388-235mm), M size: 160-135mm (428-255mm), ML size: 180-155mm (468-275mm), L size: 210-185mm (528-305mm), XL size: 210-185mm (528-305mm)

  • Saddle

    Ergon SM10 E-Mountain Black.

     

    Front brake

    Sram Maven Base, 18-millimeter 4 piston caliper, Centerline 200mm IS 6 bolts one-piece rotor, steel-backed organic pads.

  • Rear brake

    Sram Maven Base, 18-millimeter 4 piston caliper, Centerline 200mm IS 6 bolts one-piece rotor, steel-backed organic pads.

     

    Brake lever

    Sram Maven Base , Directlink and lever pivot bushings, tools-free reach adjust

  • Rims

    DT Swiss Hybrid H 1900 SPLINE, aluminum, hooked / crotchet tubeless TC, 30mm internal width, tubeless ready, 28 spokes.

     

    Spokes

    DT Swiss Straightpull spokes, DT Hybrid I, black

  • Front hub

    DT Swiss 370, Boost 15x110mm, IS 6 bolts

     

    Rear hub

    DT Swiss 370 with 18T Ratchet LN System, Boost 12x148mm, IS 6 bolts, HG freehub

  • Front tire

    Maxxis Assegai 29x2.5WT, tubeless ready, 3C Maxx Grip compound, EXO+ protection, 60TPI, folding bead

     

    Rear tire

    Maxxis Minion DHR II 27.5x2.5 WT, tubeless ready, 3C MaxxTerra compound, DD protection, 120x2TPI, folding bead

  • Crankset

    e*thirteen Helix Race e*spec alloy. All sizes: 155mm

     

    Rings

    Sram Eagle 36T T-Type, 104BCD, steel, CL55

  • Chain

    Sram GX Eagle T-Type, 12s Flattop, w/Powerlock

     

    Rear derrailleur

    Sram S1000 Eagle AXS, T-Type, 12s

  • Shift levers

    Sram Eagle AXS Pod Controller w/discrete clamp, 12s

     

    Casette

    Sram XS 1270, 10-52T, T-Type, 12s

  • Others

    Sram AXS Extension Cord / Avinox 4A Charger

     

    Weight

    22.8 Kg

  • Display

    Avinox DP100 Display / Avinox BC100 Wireless Controller (L&R)

     

    Motor

    Avinox M2S

  • Battery

    Avinox 800Wh Battery


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SKU: 34092450321

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4.8 ★★★★★
Based on 16 reviews
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Product Reviews
P
Verified Purchase
Par
Port Orchard, 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
Fort Morgan, 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
Louisville, 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
Battle Creek, 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
Fort Morgan, 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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