SKU: 18746024459

Sportful CLASSIC THERMAL JERSEY teal blu

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Description

Sportful CLASSIC THERMAL JERSEY teal bluKlassieke Warmte, Moderne Prestaties De Sportful Classic Thermal Jersey is de ideale mix van comfort, warmte en veelzijdigheid voor fietsers die in wisselende omstandigheden rijden. Dit veelzijdige wintershirt combineert tijdloos design met moderne prestatiekenmerken, perfect voor wie waarde hecht aan prestaties n uitstraling. 100% polyester voor optimaal comfort Gemaakt van 100% polyester met een geverfde afwerking voor een zachte feel en stijlvolle

Klassieke Warmte, Moderne Prestaties

De Sportful Classic Thermal Jersey is de ideale mix van comfort, warmte en veelzijdigheid voor fietsers die in wisselende omstandigheden rijden. Dit veelzijdige wintershirt combineert tijdloos design met moderne prestatiekenmerken, perfect voor wie waarde hecht aan prestaties én uitstraling.

100% polyester voor optimaal comfort

Gemaakt van 100% polyester met een geverfde afwerking voor een zachte feel en stijlvolle look. De stof voelt zacht aan op de huid en droogt snel, zodat je droog en comfortabel blijft, zelfs bij schommelende temperaturen en intensieve trainingsritten.

Hoogtepunten van de Sportful Classic Thermal Jersey

  • 100% polyester stof - voelt zacht aan en droogt snel voor optimaal comfort
  • Thermische isolatie - geschikt voor het tussenseizoen en milde winterdagen
  • Sneldrogende eigenschappen - blijf droog en comfortabel, zelfs bij wisselende temperaturen
  • Regular fit - bewegingsvrijheid en ruimte om te combineren met andere lagen of een vest
  • Klassiek ontwerp - tijdloze stijl met moderne prestatiekenmerken
  • Veelzijdig inzetbaar voor verschillende weersomstandigheden

Tijdloos comfort voor winterritten

De Classic Thermal Jersey is speciaal ontworpen voor koelere dagen in het tussenseizoen en milde winterritten. De regular fit biedt voldoende bewegingsvrijheid en ruimte om te combineren met een vest of windjack voor extra bescherming wanneer het weer omslaat.

Deze jersey is een stijlvolle en functionele toevoeging aan je fietsgarderobe - perfect voor wielrenners die op zoek zijn naar een betrouwbaar basisstuk dat zowel solo als in combinatie met andere lagen uitstekend presteert. Klassieke warmte met moderne prestaties, klaar voor elke rit.

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SKU: 18746024459

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4.2 ★★★★★
Based on 23 reviews
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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
Omaha, 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
New York, 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
Pawtucket, 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
Houston, 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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