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Description
lily flower fragrance Lily of the Valley (Muguet) Solid PerfumeInfo VERDANT BELL Lily of the Valley Solid Perfume Green Muguet, Grapefruit, Green Tea, Bergamot & Gardenia Natural Fragrance Lily of the valley in bloom fresh, green, luminous, and refined. A fresh green floral solid perfume capturing the unmistakable scent of lily of the valley (muguet)crisp, dewy, and softly radiant. Verdant Bell opens with bright green freshness, unfolding into a clean white floral heart that is airy, lightly sweet, and gently
Info
VERDANT BELL Lily of the Valley Solid Perfume – Green Muguet, Grapefruit, Green Tea, Bergamot & Gardenia Natural Fragrance
Lily of the valley in bloom — fresh, green, luminous, and refined.
A fresh green floral solid perfume capturing the unmistakable scent of lily-of-the-valley (muguet)—crisp, dewy, and softly radiant. Verdant Bell opens with bright green freshness, unfolding into a clean white floral heart that is airy, lightly sweet, and gently soapy, with a soft watery clarity. The fragrance settles into a smooth floral veil—delicate, elegant, and easy to wear—making it ideal for daily fragrance, spring perfume, weddings, gifting, and a signature floral scent. A refined muguet perfume for lovers of fresh, clean, green floral fragrances.
Top Accord – Fresh Green Stems & Citrus Lift
Fresh green stems, grapefruit, bergamot, and light botanical notes open with clean spring brightness. Crisp, airy, and verdant, without heavy sweetness.
Heart Accord – Lily of the Valley & White Florals
Lily of the valley, also known as muguet, forms the soft floral heart with gardenia and airy white petals. Fresh, green, elegant, and lightly floral.
Base Accord – Green Tea, Soft Powder & Botanical Warmth
Green tea, light powder, and a smooth botanical base give this muguet solid perfume a clean, polished finish for everyday wear.
Fragrance Experience – Fresh Floral Solid Perfume
Verdant Bell is a lily of the valley solid perfume for lovers of green floral fragrance, clean muguet perfume, fresh spring florals, and subtle botanical scent.
Format – Solid Perfume Balm
Alcohol-free, spill-free, travel-friendly solid perfume balm in a compact format for pocket, bag, dopp kit, or discreet reapplication.
Botanical Note – Lily of the Valley Muguet
Lily of the valley, or muguet, is prized in perfumery for its fresh green floral character, clean spring impression, and delicate white-floral elegance.
How to Experience the Notes
Avoid smelling directly from the tin. Apply a small amount to the back of the hand outdoors, inhale gently, and revisit during the first five minutes as the accords unfold.
Layering & Custom Blending
Layers with other solid perfumes and colognes. Try vanilla, woody, resinous, amber, or citrus scents for custom fragrance blends.
Craftsmanship & Sourcing
Handcrafted in the USA in small batches with natural plant extracts and essential oils sourced from around the world.
Highlights
VERDANT BELL Lily of the Valley Solid Perfume – Green Muguet Natural Fragrance
Scent
Fresh lily of the valley, green muguet, grapefruit, bergamot, green tea, and gardenia create a clean floral perfume balm.
Fresh Green Floral
A crisp, airy muguet perfume with green stems, white florals, soft powder, and botanical freshness.
Lily of the Valley Inspired
Classic lily of the valley fragrance character: clean, spring-like, elegant, lightly floral, and refined.
Travel-Friendly Solid Perfume Balm
Compact, spill-free, TSA-friendly balm for travel, work, evenings out, and discreet reapplication.
Alcohol-Free & Long-Lasting
Alcohol-free concentrated balm melts into skin and releases fragrance slowly without overpowering projection.
Thoughtful Giftable Scent
A versatile fragrance gift for lily of the valley, muguet, green floral, clean perfume, and botanical fragrance lovers.
Specs
Ingredients
Oils of: Coconut (Cocos Nucifera), Castor (Ricinus Communis), Jojoba (Simmondsia Chinensis), Meadowfoam (Limnanthes alba), Camellia (Camellia sinensis); Beeswax (Cera Alba), Soja Wax (Glycine max), Muguet Wax (Convallaria majalis); Verdant Bell Parfum (blend of essential oils and botanical extracts).
How to Use
Warm a small amount between fingertips and apply to wrists, neck, or hair. Reapply as desired. For skin care or massage, dilute up to 10% with carrier oil, moisturizer, or another oil-soluble cosmetic product. For hair care, mix a small amount with a leave-on conditioning product.
How to Experience the Fragrance
This fragrance activates with body heat and unfolds best on skin. Avoid smelling directly from the tin. Apply to the back of the hand outdoors, inhale gently, then revisit during the first five minutes.
Layering & Custom Blending
Mix or layer with other solid colognes and perfumes to create custom tobacco, vanilla, woody, amber, citrus, or resinous scent combinations.
Craftsmanship & Origin
Handcrafted in the USA in small batches with natural and organic plant extracts and essential oils sourced worldwide.
Warnings & Safety
Topical use only. Do not ingest or use in body cavities. Avoid eyes. Patch test before use. If irritation occurs, discontinue and wash skin with soap and warm water. Consult a medical professional if irritation persists. Highly concentrated eau de parfum product; do not overuse on the same skin area.
Shipping & Heat Advisory
May soften or melt during hot-weather shipping. If melted, refrigerate for a few hours to re-solidify before opening.
FAQs
Frequently Asked Questions
What does lily of the valley perfume smell like?
Lily of the valley perfume smells fresh, green, airy, and lightly floral with a clean spring character.
What is muguet in perfume?
Muguet is the French term for lily of the valley, a classic green white-floral note used in clean floral perfumes.
Is this perfume sweet?
No. Verdant Bell is fresh, green, clean, and floral rather than sugary or heavy.
Is this solid perfume strong?
It is noticeable, controlled, and non-overpowering; suitable for everyday wear, work, travel, or spring fragrance.
How long does the scent last?
Solid perfume can last several hours, depending on skin type and amount applied. The balm format develops slowly and wears smoothly.
Is this alcohol-free?
Yes. This alcohol-free solid perfume balm avoids spray, aerosol, and strong alcohol notes.
Is it travel-friendly?
Yes. The solid balm is spill-free, compact, TSA-friendly, and easy to carry in a pocket, bag, or travel kit.
Is this a good gift?
Yes. A strong gift choice for lily of the valley, muguet, fresh floral, green perfume, and clean fragrance lovers.
Who will love Verdant Bell?
Anyone seeking a lily of the valley solid perfume, fresh green floral fragrance, clean everyday scent, or subtle botanical perfume balm.
Can I layer this with other fragrances?
Yes. Layer with citrus, green tea, white floral, vanilla, woody, or amber fragrances for a personalized scent.
Get It FREE
Free Verdant Bell Solid Perfume – Limited-Time Offer
Receive one FREE 14g (0.5 oz) jar of Verdant Bell Solid Perfume with your order.
How to redeem:
• Add this product to your cart
• Enter code FREEMUGUETSOLIDPERFUME at checkout
• Minimum purchase of $100 required
Offer details:
• One free item per order
• Discount code may be used unlimited times, one free jar per order
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How to redeem:
• Add this product to your cart
• Enter code FREEMUGUETSOLIDPERFUME at checkout
• Minimum purchase of $100 required
Offer details:
• One free item per order
• Discount code may be used unlimited times, one free jar per order
• Valid while supplies last
• Promotion may end at any time
Also Explore
Also Explore
Explore our curated collection of solid colognes and perfumes crafted with essential oils and botanical extracts.
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4.8 ★★★★★
Based on 6 reviews
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Product Reviews
★★★★★ 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.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on April 18, 2017
★★★★★ 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
★★★★★ 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!!
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on November 30, 2025
★★★★★ 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
★★★★★ 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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