SKU: 32471422518

hyaluronic day glow hydration brightening moisturizer

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Description

hyaluronic day glow hydration brightening moisturizerBrightening DAY GLOW moisturizer with Hyaluronic Acid and star ingredients Niacinamide, Aloe Vera and Cannabis Oil that intensively hydrates the skin and helps restore its protective barrier to improve the skins natural look and feel. Hyaluronic Acid visibly plumps and helps retain the skins natural moisture to reduce the appearances of fine lines and wrinkles. PRODUCT HIGHLIGHTS MOISTURE: Seals in moisture to build the skins protective barrier and

Brightening DAY GLOW moisturizer with Hyaluronic Acid and star ingredients Niacinamide, Aloe Vera and Cannabis Oil that intensively hydrates the skin and helps restore its protective barrier to improve the skin’s natural look and feel. Hyaluronic Acid visibly plumps and helps retain the skin’s natural moisture to reduce the appearances of fine lines and wrinkles.

PRODUCT HIGHLIGHTS

MOISTURE: Seals in moisture to build the skin’s protective barrier and prevents water loss for soft and smooth skin.

HYDRATE: Sustains hydration for longer periods to fight dryness and dehydration, premature signs of aging and environmental damage.

RADIANCE: Reveals beautifully, evenly toned and glowing skin that is dewy and supple. 

CLEAN BEAUTY + FREE FROM + PLANET POSITIVE

95% Natural ingredients.
Cruelty Free.
Dermatologically tested.
Suitable for all skin types; perfect for Dry, Normal and Combination skin.

Clean Beauty commitment with responsible sourcing and formulated without specific ingredients that are known or suspected to be potentially harmful to human health and/or the environment.

Formulated Without: Parabens, Plastic Microbeads, Sulfates SLS (Sodium Lauryl Sulfate) and SLES, Formaldehydes, Phthalates, Mineral Oil, Oxybenzone, Hydroquinone, Triclosan, Triclocarban.

STAR INGREDIENTS:

Clinical formulation made with the functional and natural ingredients you love and free-from the ones you might not.

HYALURONIC ACID: Powerful moisture-binding ingredient that can hold up to 1000x its weight in water, proven to hydrate skin, retains moisture and prevents moisture loss with reduced appearance of fine lines and wrinkles.

NIACINAMIDE: Niacinamide, also known as vitamin B3, is a water-soluble vitamin proven to help visibly minimize and tighten enlarged pores, improve uneven skin, reduce the appearance of fine lines and wrinkles and diminish dullness by strengthening the skin's surface.

ALOE VERA: With cooling and anti-inflammatory properties, helps retain moisture with a protective layer for the skin.

CANNABIS OIL: Derived from Hemp and composed of up to 90% omega fatty acids which are known to provide essential moisture and nourishment while helping strengthen skin’s barrier to prevent dryness.

 

DIRECTIONS

Apply cream to clean and dry skin using upward sweeping motions. Use 1-2 times daily or as needed.

INGREDIENTS / INCI

AQUA, GLYCERIN, CAPRYLIC/CAPRIC TRIGLYCERIDE, HELIANTHUS ANNUUS SEED OIL, ETHYLHEXYL STEARATE, CETEARYL ALCOHOL, GLYCERYL STEARATE CITRATE, DIMETHICONE, NIACINAMIDE, CANNABIS SATIVA SEED OIL, GLUCOSAMINE SULFATE, UREA, ALOE BARBADENSIS LEAF JUICE, CHAMOMILLA RECUTITA FLOWER EXTRACT, SYMPHYTUM OFFICINALE ROOT EXTRACT, PANTHENOL, SODIUM HYALURONATE, TOCOPHERYL ACETATE, ALLANTOIN, PHENOXYETHANOL, ACRYLATES/C10-30 ALKYL ACRYLATE CROSSPOLYMER, TRIETHANOLAMINE, PARFUM, BHT, DISODIUM EDTA, ETHYLHEXYLGLYCERIN, CITRIC ACID, SODIUM BENZOATE, POTASSIUM SORBATE.

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SKU: 32471422518

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4.2 ★★★★★
Based on 27 reviews
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0x00000000:00000000
Waukegan, 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
Port Orchard, 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
San Leandro, 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
San Leandro, 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
Lake Worth, 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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