SKU: 25649902980
silver leaf plants for hanging baskets

silver leaf plants for hanging baskets Dichondra Silver Falls | Two Live Plants | Non-GMO

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Description

silver leaf plants for hanging baskets Dichondra Silver Falls | Two Live Plants | Non-GMODichondra Silver Falls is a stunning trailing plant, prized for its silky silver foliage and cascading growth habit. Its long, flowing stems spill beautifully from hanging baskets, containers, and garden beds, creating an elegant, waterfall like effect. The small, rounded leaves shimmer in the sunlight, providing striking contrast to bold flowering plants. Whether draping over patio planters or softening the edges of rock gardens and retaining walls,

Dichondra Silver Falls is a stunning trailing plant, prized for its silky silver foliage and cascading growth habit. Its long, flowing stems spill beautifully from hanging baskets, containers, and garden beds, creating an elegant, waterfall-like effect. The small, rounded leaves shimmer in the sunlight, providing striking contrast to bold flowering plants. Whether draping over patio planters or softening the edges of rock gardens and retaining walls, this fast-growing, low-maintenance variety is the perfect way to add texture, movement, and silvery elegance to any outdoor space.

Exceptionally heat- and drought-tolerant, Dichondra Silver Falls thrives in full sun and well-draining soil, making it ideal for hot, dry climates. This self-cleaning, low-maintenance plant requires little care while delivering season-long beauty. Whether used as a cascading accent in containers, a ground cover in garden beds, or a dramatic addition to hanging baskets, this ornamental foliage plant brings effortless charm and contrast to any landscape.

Clovers Garden Dichondra Silver Falls Plants:

  • Two Large, Live Plants – Ready to grow, premium-quality Dichondra Silver Falls, 4” to 8” tall, in 4” pots.
  • Non-GMO, No Neonicotinoids – Grown without harmful chemicals to support healthy garden ecosystems.
  • Striking Silver Foliage – Small, rounded leaves shimmer in sunlight, creating a soft, trailing effect in baskets and beds.
  • Fast-Growing & Cascading – Forms long, flowing stems that spill beautifully over containers and retaining walls.
  • Thrives in Gardens & Containers – Perfect for hanging baskets, raised beds, and mixed planters.
  • Heat & Drought-Tolerant – Flourishes in hot, dry conditions, requiring minimal upkeep.
  • Grown in the Midwest – All plants are grown in the USA, and we oversee the entire process from seed to shipment.
  • Fast Shipping & Careful Packaging – Your plants arrive quickly in our eco-friendly, 100% recyclable packaging to protect your plants and the planet.
  • Quick Start Planting Guide – A copyrighted growing guide walks you through every step, from unboxing to planting.
  • Third Generation, Family-Owned Small Business – Helping gardeners since 1957 with high-quality plants grown in the Midwest.
  • 100% Satisfaction Guaranteed

Dichondra Silver Falls - Fun Fact:
Native to regions of the southwestern U.S. and Mexico, Dichondra "Silver Falls" is prized for its striking cascades of silvery-green foliage. Gardeners often use it in hanging baskets or containers because it can grow up to four feet long, creating beautiful, trailing curtains of silver leaves.

How to Grow and Care for Dichondra Silver Falls:

  • Sunlight – Thrives in full sun, tolerating partial shade but producing the best color in bright sunlight.
  • Soil – Prefers well-draining, sandy, or rocky soil; avoid heavy, wet soils.
  • Watering – Drought-tolerant once established; water deeply but infrequently.
  • Spacing – Plant 12-18 inches apart to allow for trailing growth.
  • Growth & Maintenance – A fast-growing annual plant, reaching 3-6 feet long with cascading stems. Trim lightly to maintain shape.
  • Pest Prevention – Naturally resistant to deer and most pests, making it an excellent trouble-free garden addition.

Common Questions About Dichondra Silver Falls:

How do you grow Dichondra Silver Falls?  Plant in full sun with well-draining soil, water moderately, and trim as needed for a fuller, cascading effect.

How long do Dichondra Silver Falls' vines grow?  This trailing variety can reach 3-6 feet long, making it ideal for hanging baskets, window boxes, and garden beds.

Does Dichondra Silver Falls bloom?  While primarily grown for its silvery foliage, it may produce tiny, inconspicuous pale green flowers in warm climates.

Can Dichondra Silver Falls be grown in containers?  Yes! Its cascading habit makes it perfect for hanging baskets, patio planters, and raised beds.

Is Dichondra Silver Falls a perennial or annual?  It is a perennial in Zones 8-10 and grown as an annual in cooler climates where frost may damage the plant.

What are the best companion plants for Dichondra Silver Falls?  Dichondra Silver Falls pairs well with bold, colorful flowers that enhance its shimmering contrast. Great options include:

  • Petunias & Calibrachoa – Their vibrant blooms create a stunning contrast.
  • Salvia & Black-Eyed Susan – Adds bright pops of color for a dynamic look.
  • Lavender & Russian Sage – Complements the soft silver tones beautifully.
  • Creeping Jenny & Sweet Potato Vine – Provides contrasting foliage in mixed planters.

Pollinator & Garden Benefits - Dichondra Silver Falls is a low-maintenance, pest-resistant plant that thrives in hot, dry conditions while providing lush, cascading texture to gardens and containers. While not a major nectar source, its soft foliage adds depth to pollinator-friendly plantings, enhancing the beauty of flowering companions.

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4.6 ★★★★★
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Natrona Heights, 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
Waukegan, 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
Carnegie, 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
Dallas, 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
A
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Adam
Houston, 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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