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R250 | Opvouwbare fatbike | Combi-deal

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R250 | Opvouwbare fatbike | Combi-dealR250 Met de R250 van Moofa heb je de ideale opvouwbare elektrische fatbike voor naar school, naar het werk of voor een snelle boodschap. Dit instapmodel beschikt over een sterke 250W motor en een duurzame uitneembare accu die mooi is weggewerkt. De combinatie van motor en accu zorgen ervoor dat jij moeiteloos tot wel 80 km kunt fietsen. Het opvouwbare frame maakt hem zeer compact en erg makkelijk in gebruik doordat je deze eenvoudig mee kan nemen in

R250

Met de R250 van Moofa heb je de ideale opvouwbare elektrische fatbike voor naar school, naar het werk of voor een snelle boodschap. Dit instapmodel beschikt over een sterke 250W motor en een duurzame uitneembare accu die mooi is weggewerkt. De combinatie van motor en accu zorgen ervoor dat jij moeiteloos tot wel 80 km kunt fietsen. Het opvouwbare frame maakt hem zeer compact en erg makkelijk in gebruik doordat je deze eenvoudig mee kan nemen in het OV, maar ook makkelijk naar je werkplek. Voorzien van alle opties als led-verlichting, schijfremmen, Shimano versnellingen, voor- en achtervering, toeter, remlicht en zelfs knipperlichten, zorgt deze Moofa vouwfiets ervoor dat jij je veilig door het verkeer kunt bewegen. Tevens voorzien van een startblokkeersysteem welke je kunt in- en uitschakelen met behulp van de meegeleverde sleutels. Met zijn hoogwaardig matzwarte afwerking in combinatie met de stoere dikke banden en mooie spaakvelgen, doorbreekt de R250 de standaard van de degelijke vouwfiets.

 

De meest compacte en complete fatbike

Het opvouwbare aluminium frame van de R250 is perfect voor mensen die veel met het OV reizen en dit deels willen combineren met een stukje beweging op de fiets. Eenmaal uitgevouwen is deze opvouwbare e-fatbike een volwaardige fatbike en met behulp van het handige scharniermechanisme wordt deze handzaam en compact om mee te nemen. Voorzien van een stevige bagagedrager waar je met gemak het nodige gewicht op kan vervoeren als bijvoorbeeld een zware rugzak. Nog meer ruimte nodig? Breidt hem dan uit met een bagagerek voor.

 

Veilig en vertrouwd deelnemen aan het verkeer

De R250 van Moofa is voorzien van allerlei slimme opties die het tot de elektrische fiets maken waar wij 100% achter staan. Als Nederlands merk testen wij iedere fiets uitvoerig voordat deze ons magazijn verlaat. Dit betekent dat werkelijk alles moet kloppen. Veilige hydraulische schijfremmen, hoogwaardige versnellingen van Shimano, een claxon, led-verlichting voor en achter en zelfs een remlicht én knipperlicht. Om ervoor te zorgen dat jij langdurig gebruik kunt maken van deze elektrische vouwfiets, wordt deze standaard geleverd met startblokkeersysteem en is deze uit te breiden met een Track & Trace systeem. Mankeert er onverhoopt toch iets aan je Moofa fatbike? Dan biedt Moofa je de zekerheid van 24/7 vervanging.

 

De R-line serie van Moofa

Binnen de R-line serie zijn drie verschillende varianten verkrijgbaar. Deze R250 is het stoere matzwarte model, en er is nog het kleine broertje de R200 en de gelijkwaardige R300 die in hoogglans zwart is afgewerkt. Het verschil van de R250 ten opzichte van de R200 zit hem in hoe de accu is weggewerkt en het type velg dat wordt gebruikt. Qua afwerking zijn ze alle drie gelijkwaardig en krijg je bij Moofa het meeste waar voor je geld als het gaat om elektrische fatbikes. De R-line biedt je het comfort van een fatbike gecombineerd met het praktische van een vouwfiets. Kies vandaag nog voor de R250 opvouwbare fatbike en cruise binnenkort al over straat.


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SKU: 80872105427

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Walter Echo-Hawk, author of THE SEA OF GRASS.
Draper, 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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Verified Purchase
Par
Dallas, 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
Grantham, 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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Verified Purchase
Amazon Customer
Louisville, US
★★★★★ 4
Just learning it
Format: Paperback
Nice learning book just have to finish it
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Reviewed in the United States on December 10, 2025
K
Verified Purchase
Kindle Customer
Lake Worth, US
★★★★★ 5
Very useful book
Format: Paperback
I use it for the machine learning class I teach.
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Reviewed in the United States on May 3, 2026

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