SKU: 79784051592

fellow stagg ekg 0 9l vattenkokare

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Description

fellow stagg ekg 0 9l vattenkokarefellow stagg voor precisie, controle en elegante bereiding Fellow Stagg EKG 0,9 L is een designgerichte elektrische waterkoker voor wie thuis dezelfde controle wil als bij een doordachte koffiebarsituatie. Het combineert snelle opwarming, nauwkeurige temperatuurregeling en een smalle precisie uitloop in een ontwerp dat zowel exclusief als functioneel aanvoelt. Voor ons is dit een fellow stagg die echt het verschil maakt als je gelijkmatiger, rustiger

fellow stagg voor precisie, controle en elegante bereiding

Fellow Stagg EKG 0,9 L is een designgerichte elektrische waterkoker voor wie thuis dezelfde controle wil als bij een doordachte koffiebarsituatie. Het combineert snelle opwarming, nauwkeurige temperatuurregeling en een smalle precisie-uitloop in een ontwerp dat zowel exclusief als functioneel aanvoelt. Voor ons is dit een fellow stagg die echt het verschil maakt als je gelijkmatiger, rustiger en bewuster wilt zetten. Als fellow stagg vannkoker is hij bijzonder geschikt voor pour-over, thee en andere manuele zetmethoden waarbij stroming, tempo en temperatuur het resultaat in de kop beïnvloeden. Het is ook een fellow waterkoker voor wie roestvrijstalen materialen, een duidelijk display en een vormgeving die de hele zetplek versterkt, waardeert.

Belangrijkste functies

  • ✓  Instelbare temperatuur voor nauwkeurig zetten tussen 40°C en 100°C, zodat je de warmte kunt aanpassen aan alles van groene thee tot pour-over koffie
  • ✓  Smalle precisie-uitloop en goed uitgebalanceerd handvat zorgen voor een zachte, gecontroleerde schenking met hoge nauwkeurigheid in elke cirkelbeweging
  • ✓  LCD-scherm toont zowel de gekozen temperatuur als de actuele realtime temperatuur, waardoor het eenvoudig is de opwarming precies te volgen
  • ✓  Hold-modus houdt de gekozen temperatuur tot 60 minuten vast wanneer je rustig wilt zetten of meerdere kopjes achter elkaar wilt serveren
  • ✓  Ingebouwde zet-timer helpt je het ritme te houden bij koffie- en theezetten en zorgt voor betere herhaalbaarheid van kop tot kop

Details

Producttype Elektrische waterkoker
Model Stagg EKG elektrische waterkoker (pour-over model)
Afmetingen (L x B x H) 282 x 172 x 196 mm
Gewicht 1.400 g inclusief basis
Inhoud 0,9 L / 30 oz
Kabellengte 76,2 cm
Stekker Type-B (voor VS en Canada)
Vermogen 110–120V~ / 1000–1200W / 50–60Hz
Temperatuurbereik 40°C–100°C
Temperatuurbereik volgens eerdere productgegevens 57°C–100°C / 135°F–212°F
Temperatuurinstelling Variabele temperatuurregeling met stappen van 0,5°F
Materiaal 304 18/8 roestvrij staal
Handvat en dekselknop BPA-vrij plastic, met optie voor houten details in walnoot of esdoorn
kleur Verkrijgbaar in zwart en wit
Display LCD-scherm met gekozen temperatuur en actuele temperatuur in realtime
Warmhoudfunctie Houdt de gekozen temperatuur tot 60 minuten vast in HOLD-modus
Timer Ingebouwde zet-timer
Planning Heeft planningfunctie
Temperatuureenheid Keuze tussen Celsius en Fahrenheit
Uitloop Precisie-uitloop van het gooseneck-type voor gecontroleerd schenken
Opwarming Snelle opwarming
Basis Minimalistische elektrische basis
Certificeringen ETL, FCC, CSA, UL
Overige variantinformatie Ook beschikbaar in 0,6 L-versie voor EKG

Onderhoudsinstructies

Gebruik de waterkoker altijd samen met de meegeleverde elektrische basis en plaats deze nooit op een kookplaat of inductiekookplaat, omdat dit zowel functionele onderdelen als de afwerking kan beschadigen. Vul hem alleen met vers, schoon water. Fellow raadt af om melk of andere vloeistoffen te verwarmen, omdat dit de werking van het product negatief kan beïnvloeden. De fabrikant geeft geen volledige reinigings- of ontkalkingsroutine op de productpagina, dus we raden aan aanvullende ondersteuningsinformatie te raadplegen indien nodig. Een eerlijk punt om te vermelden is dat deze kleine waterkoker met temperatuurregeling is ontwikkeld voor precisie in plaats van grote volumes, waardoor 0,9 liter ideaal is voor koffie, thee en kleinere porties, maar minder geschikt voor grotere huishoudens tegelijk.

Over de fabrikant

Fellow ontwikkelt apparatuur voor koffie en thee met een duidelijke focus op design, precisie en doordachte materiaalkeuzes. We waarderen hoe het merk een professionele uitstraling combineert met een modern design dat past in een strak keukeninterieur. De Stagg EKG is ontworpen als een vanzelfsprekende partner op het aanrecht voor wie op zoek is naar een betere kop en meer controle wil over het hele brouwproces, van temperatuur tot giettempo.

Als je een waterkoker voor koffie wilt kopen die ook een sterke kandidaat is voor de beste waterkoker voor thee, is dit een van onze meest overtuigende keuzes voor serieuze thuisbrouwers met oog voor design.

Veelgestelde vragen

▾Wat maakt de Fellow Stagg bijzonder geschikt voor pour-over koffie?

De smalle precisietuit en het goed uitgebalanceerde handvat geven zeer fijne controle over de stroom en giettempo, wat cruciaal is als je gelijkmatig en precies met de hand wilt zetten.

▾Heeft deze Fellow Stagg waterkoker een instelbare temperatuur?

Ja, de temperatuur is instelbaar tussen 40°C en 100°C en wordt duidelijk weergegeven op het LCD-scherm samen met de actuele realtime temperatuur.

▾Kan ik de Fellow Stagg EKG met andere vloeistoffen dan water gebruiken?

Nee, deze is alleen bedoeld voor vers, schoon water en moet worden gebruikt in combinatie met de elektrische basis. Melk of andere vloeistoffen kunnen de waterkoker beschadigen en worden daarom niet aanbevolen.

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4.6 ★★★★★
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Natrona Heights, US
★★★★★ 5
Excellent book, possibly currently unique in coverage of latest ideas
This book is possibly currently unique in its coverage of the latest ideas in the field of deep learning -- and it is a very convenient and good survey of fundamental concepts (linear algebra, optimization, performance metrics, activation function types), different network types (multi-layer perceptron, convolutional neural networks, and recurrent neural networks), practical considerations (data set, training and validation, implementation), and applications (comments on existing real-world/commercial uses). The final 235 pages of the content portion of the book is dedicated to topics in "Deep Learning Research", and these topics are truly at the current frontier. Another reviewer said that one could gain the same knowledge of cutting-edge research by reading all of the latest papers (from academia and industry), but the "research" section of this book offers the following: Selection of the most notable research by the very experienced authors of the book, and collection of similar research in to a broader discussion of themes, and the additional insights. The book covers very advanced and new ideas currently being explored, and it is very nice to be able to have a consistent and coherent presentation of all of those ideas. However, the book is also packed with valuable observations and pointers about more basic aspects of deep learning implementations and practices -- and such commentary is in depth and includes substantial analysis and mathematical derivation (in an intuitive presentation that often includes graphs illustrating the phenomenon). As someone with an intermediate level of knowledge and experience of neural networks, I am really grateful for this book, because seems like the ideal resource for learning cutting-edge ideas and practices, with context. The book has excellent scope and depth, and I am confident that anyone with a solid background in linear algebra, calculus, statistics, and general machine learning, and basic neural networks (multi-layer perceptrons) will find this book to be very exciting and perhaps unique in its ability to take the reader to the next level and a new frontier. I was personally excited to learn about the idea of representing the dependencies of intermediate quantities by directed graphs, and how this can be used to perform calculations for recurrent neural networks efficiently. And I think the long chapter on recurrent neural networks is very helpful. Having said all of this, I think only people with significant working knowledge and experience with neural networks and mathematics -- people whose academic or professional focus has been neural networks for at least a year or two -- would benefit from this book. This book answers a lot of the deeper questions that one is likely to have while developing a solid understanding of the fundamentals, and that's one of the book's tremendous values, but this book assumes an understanding of the fundamentals (but does briskly cover the basics). I think this book is a perfect follow-up book for the excellent book "Neural Network Design (2nd edition)" by Hagan, Demuth, Beale, and de Jesus, and I highly recommend the latter for gaining the solid background needed to have a thrilling experience with the "Deep Learning" book. In summary, I am very glad this "Deep Learning" book was written, and I think the "Deep Learning" book will be a great benefit to a lot of people, and to the evolution of the field.
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Reviewed in the United States on April 18, 2017
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Zygerian99
Omaha, US
★★★★★ 5
The definitive guide to becoming a researcher in the field
Format: Hardcover
This is not a coding book. I see a lot of negative reviews around the expectation that this book would teach the reader how to quickly build machine learning systems and write code. This book is not for that audience. If you just want to build applications, don't worry about how deep learning works. It's akin to needing to understand how an engine works just to drive a car. If you are looking for a coding resource, try: https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1492032646/ref=sr_1_4?keywords=machine+learning+tensorflow&qid=1579608765&sr=8-4 . And even with that book, the material still goes far beyond what you need - use it as a light reference. I bought this book as an aspiring machine learning researcher, and towards that end, it is the best resource available in print (still true as of 2020). For instance: The first 5 chapters are timeless. These are things that were mostly established 20 or 30 years ago and beyond and are mostly STEM fundamentals at this point. There are whole textbooks dedicated to each of those chapters, but the authors provide a quick refresher and overview of probably 80% of what you'll encounter in deep learning. If you haven't previously learned each of these subtopics, you'll probably want to study them individually since they are the key to innovating (linear algebra, probability & stats, numerical computation, machine learning fundamentals). Chapters 6 thru 9 are the foundation of deep learning. We're about 12 years into seeing rapid change in the deep learning space, yet all of these principles and techniques still hold (many recent innovations are still relying on Convolutional models in 2020, which is the most layered/complex topics in those chapters). Therefore, I'd wager that these chapters are also fairly stable knowledge that is worth internalizing if you want to be deeply involved in the future of machine learning. Chapters after 9 are mostly experimental topics, and many of them are already the wrong strategies for optimal results. But there are interesting ideas in here that you'll often encounter in the wild, so it's good exposure to various topics. But probably not worth much of your time. And lastly, there is good history in here from people who know the space intimately. It's a good way to piece together the developments and learn the lexicon of deep learning so you can have intelligent conversation with experts.
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Reviewed in the United States on January 21, 2020
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Shannon
Port Orchard, US
★★★★★ 5
The best DL/ML book I have ever seen!!
Format: Hardcover
Fantastic deep-learning book! The logic is very easy to follow, but the content is very thorough when it comes to explaining the theories behind it, making it perfect for beginners as well as math and CS students. The best DL/ML book I have ever seen!!
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Reviewed in the United States on November 30, 2025
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William P Ross
Port Orchard, US
★★★★★ 5
Comprehensive Look At An Incredibly Complex Topic
Format: Hardcover
Deep Learning is an advanced book with great explanations and details. There is a heavy math focus with the book's beginning chapters detailing the necessary linear algebra and probability that one will need to understand deep learning. I liked that the author's chose to cover only the parts of these subjects which are relevant to deep learning. There are many interesting philosophical sections in the book as well. Just about when I was feeling overwhelmed with the complexity of the mathematics the authors take a step back and cover the foundations of deep learning such as borrowing concepts from human learning. There was an interesting dicussion about the early studies done on the vision of cat's and monkey's in the 1970s. The text covers the entire history of deep learning and the bibliography is hundreds of sources. It is clear this is the most comprehensive text available about deep learning. For anybody interested in this topic this book is a mandatory read. There are sections about machine learning as well, which makes sense because deep learning is a subset of machine learning. These sections focused on the machine learning concepts which are most relevant to deep learning. The book was well organized and divided into three parts which cover mathematics related to deep learning, typical deep learning techniques, and then more experiment learning techniques. Often the author's state when a technique works well or when it does not, and which types of data works best for the technique. Just a warning, the math in this book is highly complex. It requires a lot of work to go through this book, but the effort will be well rewarded.
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Reviewed in the United States on March 15, 2017
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Adam
Whiting, US
★★★★★ 4
Too Dry.
Format: Hardcover
This was a required textbook for my class in college. I think it was too dry. The book titled Deep Learning: From Curiosity To Mastery is much more approachable.
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Reviewed in the United States on May 22, 2026

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