SKU: 24942318937

mac studio radiance 24hr luminous lift concealer 11ml

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Description

mac studio radiance 24hr luminous lift concealer 11mlLeiden Sie unter dunklen Augenringen, die Sie mde aussehen lassen, obwohl Sie ausgeruht sind? Der MAC Cosmetics Studio Radiance 24hr Luminous Lift Concealer 11ml deckt diese ab, ohne wie Make up zu wirken. Ihre Haut sieht den ganzen Tag wach und frisch aus. Das ist der Concealer, den Sie morgens schnell auftragen und den ganzen Tag nicht mehr anrhren mssen. Was sind die wichtigsten Eigenschaften des MAC Cosmetics Studio Radiance 24hr Luminous Lift

Leiden Sie unter dunklen Augenringen, die Sie müde aussehen lassen, obwohl Sie ausgeruht sind? Der MAC Cosmetics Studio Radiance 24hr Luminous Lift Concealer - 11ml deckt diese ab, ohne wie Make-up zu wirken. Ihre Haut sieht den ganzen Tag wach und frisch aus. Das ist der Concealer, den Sie morgens schnell auftragen und den ganzen Tag nicht mehr anrühren müssen.

Was sind die wichtigsten Eigenschaften des MAC Cosmetics Studio Radiance 24hr Luminous Lift Concealer - 11ml?

Wenn Sie einen Concealer suchen, der pflegt und abdeckt, ist dieser aus der Concealer Make-up-Kollektion eine echte Besonderheit. Er verbindet Make-up und Hautpflege in einem Produkt.

  • Deckt dunkle Ringe und Rötungen ab
  • Verleiht Ihrer Haut einen strahlenden, frischen Glow
  • Fühlt sich leicht an, nie schwer oder dick
  • Hält den ganzen Tag ohne zu verlaufen
  • Spendet Feuchtigkeit während des Tragens
  • Betont keine feinen Linien
  • Dermatologisch und augenärztlich getestet
  • Erhältlich in Farbton N18, abgestimmt auf die Studio RaDiance Foundation-Linie

Wie verwende ich den MAC Cosmetics Studio RaDiance Concealer?

  1. Wischen Sie überschüssiges Produkt vom Applikator ab, um zu viel auf einmal zu vermeiden.
  2. Tragen Sie den Concealer an den gewünschten Stellen auf: unter den Augen, neben der Nase oder auf Rötungen.
  3. Tupfen Sie ihn vorsichtig mit dem Ringfinger oder einem kleinen Pinsel ein, so verschmilzt er schön mit der Haut.
  4. Für mehr Deckkraft tragen Sie eine zweite dünne Schicht auf, statt mehr Produkt auf einmal.
  5. Bei fettiger Haut oder tiefen Linien leicht mit transparentem Puder fixieren.

Eine gute Grundierung unter den Augen hilft, den Concealer besser zu fixieren. Die 24-Hour Extend Eye Base von MAC ist dafür ideal: Sie sorgt dafür, dass der Concealer weniger in feine Linien zieht und länger hält.

Warum wirkt dieser strahlende Concealer so gut?

Ihre Haut fühlt sich sofort weicher und voller unter den Augen an. Das liegt an Hyaluronsäure in der Formel, die Feuchtigkeit bindet und die Haut glatter erscheinen lässt. Ihr Teint wird bei längerer Anwendung auch ebenmäßiger. Niacinamid unterstützt, indem es Pigmentierung mindert und die Haut stärkt. Zusammen bieten sie einen Concealer, der nicht nur abdeckt, sondern Ihrer Haut auch etwas zurückgibt. Die MAC Cosmetics Studio RaDiance Linie wurde speziell für einen strahlenden Finish ohne Make-up-Effekt entwickelt. Das spüren Sie bereits beim ersten Auftragen.

Ist der MAC Studio RaDiance Concealer für trockene Haut unter den Augen geeignet?

Ja, dieser Concealer ist eine gute Wahl für trockene Haut. Die Formel enthält mehrere feuchtigkeitsspendende Inhaltsstoffe, die die Haut geschmeidig halten.

Die Haut unter den Augen ist dünner und trockener als im restlichen Gesicht. Schwere oder Matte Concealer betonen das oft. Diese Formel fühlt sich leicht an und spendet keine Spannung, sodass sie den ganzen Tag angenehm sitzt. Puder ist bei trockener Haut nicht notwendig, kann aber für extra Sicherheit verwendet werden.

Hält der MAC Cosmetics Studio Radiance 24hr Luminous Lift Concealer - 11ml wirklich 24 Stunden?

In den meisten Fällen ja. Der Concealer bleibt den ganzen Tag gut an Ort und Stelle, ohne zu verschmieren oder zu verblassen.

Wichtig ist: Nie zu viel Produkt auf einmal verwenden. Eine dünne Schicht hält besser als eine dicke. Bei fettiger Haut oder tiefen Linien hilft ein Hauch transparentes Puder, um Creasing zu vermeiden. Für die meisten Hauttypen ist das nicht nötig.

Wirken Niacinamid und Vitamin C wirklich gegen dunkle Augenringe?

Sie unterstützen, aber erwarten Sie keine sofortige Wirkung. Niacinamid und Vitamin C arbeiten langsam gegen Pigmentierung, der Effekt ist nicht nach einer Anwendung sichtbar.

Bei täglicher Anwendung der mac studio raDiance Concealer kann sich die Pigmentierung unter den Augen nach einigen Wochen reduzieren. MAC berichtet von einer Verringerung der dunklen Ringe nach zwölf Wochen Anwendung. Der Soforteffekt ist vor allem der strahlende Look, der die Augen frischer wirken lässt. Das merken Sie sofort, die nachhaltige Wirkung zeigt sich bei regelmäßiger Anwendung. Auch in Rezensionen zum MAC Concealer wird dieser Unterschied oft erwähnt.

MAC Studio RaDiance versus andere strahlende Concealer

Wollen Sie wissen, ob die mac concealer studio finish-Optik wirklich anders ist als die anderer Marken? Ist sie. Während der NARS RaDiant Creamy Concealer etwas deckender und dicker ist, ist der MAC Studio RaDiance bewusst leicht und serumähnlich in der Textur. Charlotte Tilburys Beautiful Skin Concealer schenkt ebenfalls schönen Glow, aber die Haltbarkeit ist kürzer. Wenn Sie einen Concealer suchen, der den ganzen Tag hält und Ihre Haut strahlen lässt, ist der MAC Cosmetics Studio Radiance 24hr Luminous Lift Concealer - 11ml die beste Wahl in diesem Segment.

Bei sehr dunklen Augenringen, die komplett abgedeckt werden sollen, ist ein vollständig deckender, Matte Concealer effektiver. Für einen frischen, natürlichen Look, der den ganzen Tag hält, brauchen Sie genau diesen Concealer. Sie können den mac studio raDiance Concealer in der MAC Concealer-Kollektion auf beautykaufen.de kaufen.

Inhaltsstoffe & weitere Informationen

Water\Aqua\Eau, Phenyl Trimethicone, TrimethylSiloxySilicate, Methyl Trimethicone, Glycerin, C9–12 Alkane, Niacinamide, Butylene Glycol, Neopentyl Glycol Diheptanoate, Ascorbyl GlucoSide, Lauryl Peg–9 PolydimethylSiloxyethyl Dimethicone, Silica, Olea Europaea (Olive) Fruit Oil, SimmondSia ChinenSis (Jojoba) Seed Oil, Hydrogenated Polyisobutene, PolySilicone–11, Hydroxyethyl Urea, Algae Extract, Tocopheryl Acetate, Sodium Pca, Helianthus Annuus (Sunflower) Seed Extract, Caffeine, Sodium Hyaluronate, Hordeum Vulgare Extract \ Extrait D’Orge, Trametes VerSicolor Extract, Cucumis Sativus (Cucumber) Fruit Extract, Peg–10 Dimethicone, Propylene Glycol Dicaprate, Sorbitol, Caprylyl Glycol, Trehalose, Triethyl Citrate, DipotasSium Glycyrrhizate, Salicylic Acid, Sodium Chloride, Vinyl Dimethicone / Methicone Silsesquioxane Crosspolymer, TriethoxycaprylylSilane, DisTeardimonium Hectorite, Ammonium Acryloyldimethyltaurate / Vp Copolymer, Dimethicone / Peg–10/15 Crosspolymer, Alumina, Lecithin, Zinc STearate, Propylene Glycol Caprylate, Sodium Hydroxide, Sodium Citrate, Laureth–7, Synthetic Fluorphlogopite, MagneSium Aluminum Silicate, Hexylene Glycol, Disodium Edta, Citric Acid, Dipropylene Glycol, Tin Oxide, Resveratrol, Phenoxyethanol, PotasSium Sorbate, [+/– Titanium Dioxide (Ci 77891), Iron Oxides (Ci 77492), Iron Oxides (Ci 77491), Iron Oxides (Ci 77499)] Iln52550

Wir empfehlen, vor Verwendung stets die Zutatenliste auf der Verpackung zu prüfen, um die aktuellsten Informationen zu erhalten. Aufgrund von Produktaktualisierungen und Optimierungen können die hier genannten Inhaltsstoffe von denen auf der Verpackung abweichen.

Herstellerkontakt:

WhitMan LBS NV
Nijverheidstraat 15
2260 Oevel
Belgien
[email protected]

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SKU: 24942318937

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4.6 ★★★★★
Based on 22 reviews
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0x00000000:00000000
Birmingham, 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
Birmingham, 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
Cuba, 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
W
Verified Purchase
William P Ross
Boise, 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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Verified Purchase
Adam
Belleville, 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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