SKU: 73772416428

Poezen met bloemen - schilderen op nummers

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Poezen met bloemen - schilderen op nummersPoezen met bloemen schilderen op nummers Op dit paint by numbers schilderij zie je twee zwarte poezen kijkend naar de zon in een weiland vol bloemetjes. Door de mooie kleuren krijg je direct een vrolijk gevoel van dit schilderij. Poezen zijn de meest gehouden huisdieren in Nederland, ook zijn zij n van de oudste huisdieren ter wereld. In Nederland leven ongeveer 3,9 miljoen poezen en katten. Wist je dat de oudste kat ter wereld 38 jaar oud is

Poezen met bloemen - schilderen op nummers

Op dit paint by numbers schilderij zie je twee zwarte poezen kijkend naar de zon in een weiland vol bloemetjes. Door de mooie kleuren krijg je direct een vrolijk gevoel van dit schilderij. Poezen zijn de meest gehouden huisdieren in Nederland, ook zijn zij één van de oudste huisdieren ter wereld. In Nederland leven ongeveer 3,9 miljoen poezen en katten. Wist je dat de oudste kat ter wereld 38 jaar oud is geworden? Schilderen op nummers is ontspanning en met deze twee poezen ben je wel een tijdje bezig. Durf jij dit project aan?

Inhoud pakket schilderen op nummers poezen met bloemen

Bij al onze schilderen op nummers pakketten ontvang je meerdere kwasten, acrylverf en het allerbelangrijkste het doek. Op het doek zijn nummertjes geprint die je helemaal zelf kunt inkleuren met acrylverf. Dit lijkt makkelijker dan het is. Er is veel geduld en doorzettingsvermogen nodig om dit schilderij helemaal af te maken. Durf jij het aan? Je ontvangt per schilderen op nummers doek:

  • 3 penselen
  • Verf (mengen is niet nodig)
  • Handleiding (NL-EN-FR)
  • Doek afmeting: 50 x 40 cm

Moeilijkheidsgraad

Schilderen op nummers

Schilderen op nummers ook wel Paint by Numbers, Schilderen volgens nummer en Nummer Schilderen genoemd, is een leuke hobby die helemaal terug is van weggeweest. Vroeger was schilderen op nummers echt iets voor kinderen maar nu hebben wij versies speciaal voor volwassenen. Een leuke bezigheid waarbij het resultaat altijd verbluffend is. Je hoeft geen schildertalent te zijn om een mooi schilderij te maken. Probeer deze rustgevende en ontspannende hobby een keer uit en je bent in no-time verslaafd. Ook leuk om als cadeau te geven. Door de vele verschillende categorieën zit er altijd iets voor je bij.

Schilderen op nummers werkt stressverlagend en ontspannend

Wist je dat schilderen op nummers / paint by numbers goed is voor je gezondheid en het zeer ontspannend is? Het schilderen leidt je af van je dagelijkse zorgen en stress. Het verlicht stress zoals spierspanning, gewrichtspijn, hoofdpijn en andere lichamelijke klachten. Het heeft een kalmerend en bijna therapeutisch effect. Veel therapeuten raden schilderen aan wanneer je last hebt van stress of burn-out klachten. Dus mocht je hier last van hebben, probeer het eens! Het is een ideale hobby om heerlijk tot rust te komen. Daarnaast is het ook nog eens goed voor je concentratie en fijne motoriek. Het invullen van de vakjes die voorzien zijn van een nummer zijn soms erg klein en daarom is uiterste concentratie erg belangrijk. Doordat je je concentreert op de details van je schilderij bouw je essentiële vaardigheden in focus op. Deze vaardigheid kun je goed gebruiken in je dagelijkse leven.

Schilderen op nummers goed voor uren schilderplezier!

Schilderen op nummers / paint by numbers voor volwassenen lijkt makkelijker dan het is. Hoe hoger de moeilijkheidsgraad hoe meer en hoe kleiner de schilder vlakken zullen zijn. Voor nummers schilderen is opperste concentratie vereist, waardoor je niet binnen een paar uurtjes klaar bent. Afhankelijk van de moeilijkheidsgraad ben je gemiddeld 25-40 uur per schilderij bezig. Ben je eenmaal klaar dan mag je echt trots zijn op het resultaat!

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4.1 ★★★★★
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Whiting, 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
Carnegie, 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
Louisville, 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
Phoenix, 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
Lowell, 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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