SKU: 17491629797

Super Skunk AutoFem - 3+1 | 5+2 ACTIE

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Description

Super Skunk AutoFem - 3+1 | 5+2 ACTIESuper Skunk Autoflowering: De krachtige klassieker, nu nog makkelijker te kweken. Op zoek naar die onmiskenbare, doordringende skunk geur gecombineerd met het gemak van een autoflower? Super Skunk Autoflowering is de verbeterde versie van een absolute klassieker. Deze stevige en productieve plant levert alles waar Super Skunk beroemd om is de krachtige high, de volle smaak en de betrouwbare groei maar dan in een compact, snelbloeiend en autoflowerend

Super Skunk Autoflowering: De krachtige klassieker, nu nog makkelijker te kweken.

Op zoek naar die onmiskenbare, doordringende skunk-geur gecombineerd met het gemak van een autoflower? Super Skunk Autoflowering is de verbeterde versie van een absolute klassieker. Deze stevige en productieve plant levert alles waar Super Skunk beroemd om is – de krachtige high, de volle smaak en de betrouwbare groei – maar dan in een compact, snelbloeiend en autoflowerend formaat. Perfect voor iedereen die wil genieten van een iconische strain zonder ingewikkeld lichtschema.

Waarom zou je voor Super Skunk Autoflowering kiezen?

  • Iconische skunk-smaak: Een heerlijk sterke, doordringende skunky geur en smaak die generaties kwekers heeft veroverd.
  • Krachtige, evenwichtige high: Een diepe lichamelijke ontspanning gecombineerd met een helder, cerebraal effect (THC 16-20%).
  • Supersnelle bloei: Met slechts 7 weken van zaad tot oogst is ze een van de snelste autoflowers.
  • Compact en productief: Blijft lekker klein (40-70 cm) maar levert verrassend veel op.
  • Vergevingsgezind en sterk: Ideaal voor beginners, maar geliefd bij iedereen vanwege haar robuuste karakter.

Waarom DR.Smartshop? Bij DR.Smartshop geloven we dat klassiekers gewoon goed moeten zijn. Daarom bieden we Super Skunk Autoflowering aan van de hoogste kwaliteit, zorgvuldig geselecteerd voor optimale groei en effect. Je profiteert van scherpe prijzen, snelle en discrete verzending, en een team van enthousiaste specialisten dat klaarstaat met deskundig advies. Of je nu net begint of al jaren kweekt, wij helpen je graag. Bestel nu en ontdek waarom Super Skunk al decennialang een favoriet is!

Genetische basis: Skunk x Ruderalis

Super Skunk Autoflowering is de trotse dochter van een van de meest bekroonde en geliefde cannabissoorten ooit. Door de krachtige, productieve genetica van Super Skunk te kruisen met een robuuste ruderalis, is een autoflower ontstaan die alles heeft wat de origineel zo beroemd maakte, maar dan nóg makkelijker te kweken. Het resultaat is een overwegend Indica-dominante plant (80%) die compact blijft, snel bloeit en barst van de hars en geur.

Effecten en smaken

Deze autoflower levert precies wat je van een Super Skunk mag verwachten: een onmiskenbaar sterk, skunky aroma dat doet denken aan de gouden dagen van de cannabisteelt. De smaak is vol, aards en doordringend, precies zoals het hoort. Het effect is een perfecte balans: een diepe, ontspannende lichamelijke stone, gecombineerd met een heldere, cerebrale high. Dit maakt haar ideaal voor zowel recreatief gebruik als voor medicinale toepassingen, zoals het verlichten van stress, chronische pijn en het stimuleren van de eetlust.

Kweekgemak en opbrengsten

Super Skunk Autoflowering is een droom om te kweken. Ze is compact, robuust en vraagt weinig onderhoud, waardoor ze perfect is voor beginners en drukke kwekers. Ze gedijt zowel binnen als buiten en beloont je met dichte, harsrijke toppen.

  • Hoogte: 40 – 70 cm – compact en discreet, ideaal voor kleine ruimtes.
  • Opbrengst binnen: Tot 140 gram per m².
  • Opbrengst buiten: 30 – 60 gram per plant.
  • Bloeitijd: Supersnelle 7 weken van ontkieming tot oogst.
  • Kenmerken: Zeer geschikt voor beginners, minimale voeding nodig, rijkelijk bedekt met hars.

Veelgestelde vragen

Wat is het verschil tussen feminized en autoflowering zaden
Feminized zaden zijn gegarandeerd vrouwelijk, dus ze produceren altijd bloemen. Autoflowering zaden bloeien automatisch na een bepaalde tijd, ongeacht het lichtschema. Super Skunk Autoflowering is zowel feminized als autoflowering: het makkelijkste van beide werelden!

Hoeveel THC bevat Super Skunk Autoflowering?
Deze krachtpatser heeft een THC-gehalte tussen de 16% en 20%, wat zorgt voor een krachtige, evenwichtige high die zowel recreatieve als medicinale gebruikers zal bekoren.

Kan ik Super Skunk Autoflowering buiten kweken?
Jazeker! Ze gedijt uitstekend buiten, vooral in warmere klimaten. Haar compacte formaat maakt haar discreet genoeg voor een balkon, terras of in de volle grond.

Teelt tips voor beginners

  • Verlichting: Sterke LED of HPS-lampen zorgen voor optimale groei en dichte toppen.
  • Voeding: Super Skunk heeft minimale aanvullende voeding nodig. Minder is vaak meer!
  • Water: Zorg voor potten met voldoende ruimte en een goede afwatering om wortelrot te voorkomen.
  • Temperatuur: Houd het lekker warm, tussen de 20-25°C, voor de beste resultaten.

Ben je klaar om een van de meest iconische cannabissoorten aller tijden te kweken? Bestel de Super Skunk Autoflowering vandaag nog bij DR.Smartshop en ontdek zelf waarom deze klassieker nooit uit de mode raakt!

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4.2 ★★★★★
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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
Massapequa, 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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Verified Purchase
Shannon
Pawtucket, 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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Verified Purchase
William P Ross
Chelsea, 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.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on May 22, 2026

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