SKU: 61525645458
zündapp alu city damen e bike green 3.0 26 zoll

zündapp alu city damen e bike green 3.0 26 zoll Zündapp Green 2.7 Damen E Bike 28 Zoll – Zündapp Shop

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zündapp alu city damen e bike green 3.0 26 zoll Zündapp Green 2.7 Damen E Bike 28 Zoll – Zündapp ShopDas zuverlssige 28 Zoll Damen E Bike Zndapp Green 2. 7 wei mit starkem Antrieb und robusten Komponenten zu berzeugen. Ausgestattet mit Tiefeinsteiger Rahmen, gekrpftem Lenker und breitem Sattel mit gefederter Sattelsttze, vermittelt das Elektrofahrrad viel Fahrkomfort. Die aufrechte Sitzposition erlaubt viel berblick und ermdungsfreies Fahren. Der starke Pedelec Motor am Vorderrad untersttzt das E Fahrrad bis 25 km h, die Untersttzungsstufen werden am

Das zuverlässige 28 Zoll Damen E Bike Zündapp Green 2.7 weiß mit starkem Antrieb und robusten Komponenten zu überzeugen. Ausgestattet mit Tiefeinsteiger-Rahmen, gekröpftem Lenker und breitem Sattel mit gefederter Sattelstütze, vermittelt das Elektrofahrrad viel Fahrkomfort. Die aufrechte Sitzposition erlaubt viel Überblick und ermüdungsfreies Fahren. 

Der starke Pedelec Motor am Vorderrad unterstützt das E Fahrrad bis 25 km/h, die Unterstützungsstufen werden am LCD-Display ausgewählt. Der Akku ermöglicht mit 375 Wh Kapazität eine Reichweite zwischen 30 und 115 km. Da neben dem Gesamtgewicht auch das gewählte Unterstützungslevel, die Geländebedingungen und Wetterverhältnisse wie beispielsweise Gegenwind Einfluss nehmen, ist eine exakte Angabe der Reichweite von E-Bike Akkus kaum möglich. Unsere Angaben sollen daher als Richtwert verstanden werden.

Das Zündapp Green 2.7 ist einem Hollandrad nachempfunden, besitzt aber eine hochwertige Shimano Nexus Nabenschaltung mit 3 Gängen und Drehgriffschalter, die im Alltagseinsatz genügend Abstufungen bieten. Das E-Bike bremst mit V-Brakes und einer Rücktrittbremse und fährt auf leichtläufigen Kenda Khan Straßenreifen. 

Das Fahrrad wird mit kompletter Ausstattung geliefert, sodass vor der ersten Fahrt kein zusätzliches Zubehör gekauft werden muss. Neben Gepäckträger, Schutzblechen und Kettenschutz sind auch ein Seitenständer sowie verkehrssichere Beleuchtung gemäß StVZO vormontiert. 

Wir empfehlen das City E Bike Personen mit einer Körpergröße zwischen 150 und 175 cm. Das E-Bike wird zu 98 % vormontiert ausgeliefert. Nach einer kurzen Endmontage des Lenkers sowie einer Prüfung von Schrauben und Schaltung sowie Bremsen können Sie direkt losfahren.

Die Nürnberger Traditionsmarke Zündapp, gegründet 1917, machte vor allem durch die Produktion von erschwinglichen Leichtkrafträdern “für jedermann” auf sich aufmerksam, die sich besonders durch ihre Zuverlässigkeit und die für damalige Verhältnisse hochmoderne Technik auszeichneten. Heute werden unter dem Namen Zündapp Fahrräder und - in Anlehnung an die Motorradherstellung - natürlich moderne E-Bikes sowie Ersatzteile produziert. Alle Räder, ob E-Bike, Klapprad oder Mountainbike, entsprechen den traditionellen Standards kombiniert mit moderner Technik. 

Das Credo lautet: preiswert, zuverlässig und unkompliziert - eben Fahrräder für jedermann.

Technische Daten:
Hersteller: Zündapp
Modell: Green 2.7
Farbe: grau, schwarz/blau
Gänge: 3
Rahmengröße: 48 cm
Laufradgröße: 700c / 28 Zoll
Rahmen: Zündapp Aluminium Tiefeinsteiger
Gabel: Zündapp Stahl Starrgabel
Steuersatz: 1" halbintegriert
Vorbau: Zündapp Aluminium, Höhe: 180 mm, Länge: 90 mm, Durchmesser: 25,4 mm, winkelverstellbar 10° bis 50°
Lenker: Zündapp Citylenker, Stahl, Breite: 600 mm, Klemmung: 25,4 mm, Durchmesser: 22,2 mm
Griffe: Steckgriff Kunststoff 90/120 mm Länge, ergonomisch geformt
Schalthebel: Shimano Nexus SL-3S41E RevoShift Drehgriffschalter 3-fach
Bremshebel: Aluminium Dreifingertyp
Kurbelgarnitur: Zündapp einfach, 38 Zähne, Kurbelarme: 170 mm
Ritzel: Einfachritzel für Getriebenabe, 19 Zähne
Kette: KMC S1 1/2" x 1/8" ohne Verschlussglied
Bremsen: Promax TX117 V-Brakes, Rücktrittbremse
Reifen: Kenda 28" x 2,0" / 700c x 50c / 50-622 mit Straßenprofil und Reflexstreifen
Felgen: Zündapp Aluminium Doublewall
Naben: Shimano Nexus SG-3C41 3 Gang Getriebenabe hinten
Pedale: Kunststoff Plattformpedale 9/16" Achse
Sattel: Selle Royal Vivo Komfortsattel
Sattelstütze: Zündapp Aluminium Federsattelstütze, Durchmesser: 27,2 mm, Länge: 300 mm, Federweg: 30 mm
Gepäckträger: Aluminium schwarz
Schutzbleche: Kunststoff
Kettenschutz: Kunststoff, halbe Kettenabdeckung
Beleuchtung: LED gemäß StVZO
Motor: Ananda F129 Radnabenmotor vorne, 36 V, 250 W, max. 40 Nm
Trittunterstützung: bis max. 25 km/h
Akku: Greenway Sitzrohrmontage, 37 V, 14,5 Ah, 536 Wh, 3,1 kg
Reichweite: 30 - 130 km je nach Zuladung und Fahrweise
Ladedauer: 6,5 h je nach Ladegerät
Display: Ananda D16 2,4" TFT mit Remote, Bluetooth und Ananda Ride App
Unterstützungsstufen: 5 + Schiebehilfe
empfohlene Körpergröße: 150 - 175 cm
Lenkerhöhe vom Boden: 99 - 106 cm
Sattelhöhe vom Boden: 93 - 104 cm
Überstandshöhe: 39 cm
zulässiges Gesamtgewicht: 120 kg
Gewicht: 23,1
Liefer 98 % vormontiert. Lenker geradestellen, Pedale montieren, Schaltung, Bremsen und Schrauben prüfen
Lieferumfang: 1 Fahrrad, Zubehör (Reflektoren, Glocke, Seitenständer, Schutzbleche, Kettenschutz, Gepäckträger, Beleuchtung, Ladegerät, Betriebsanleitung)

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Carol
Grantham, US
★★★★★ 5
Need to read book
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The truth about the Native people. THANK YOU Kent for writing this book. We purchased about 12 total.
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Reviewed in the United States on November 24, 2019
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Walter Echo-Hawk, author of THE SEA OF GRASS.
Lowell, US
★★★★★ 5
Native American history at its best!
Format: Hardcover
Kent Blansett's engrossing story about the life & times of the famed Mohawk activist Richard Oakes is Native American history at its best. I appreciated the well-written context provided about the birth, growth and impact of the Red Power Movement and the pivotal role that social justice activism played in the rise of modern Indian nations in the United States today. This scholarly work helps us understand modern Native America and is a "must-read" for every Native American Studies student and scholar, as well as readers interested in important American social justice movements.
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Reviewed in the United States on April 1, 2019
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Par
Los Angeles, 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.
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Reviewed in the United States on December 20, 2024
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Richard Hackathorn
Belleville, 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.
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Reviewed in the United States on February 26, 2022
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Amazon Customer
Natrona Heights, US
★★★★★ 4
Just learning it
Format: Paperback
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