SKU: 59094985843
black pearl asiatic lily

black pearl asiatic lily Lily

$24.74 $27.49
Save 10%
No.2 Bestseller on mvanhaasteren.nl >

Pay in installments of $6.87 with ShopPay, AfterPay and Klarna

Shipping Estimate
USA
  • USA
  • CAN

Ships within 48 hours · Estimated delivery Nov 1 - Nov 6

Promo Codes Available:

For Your Every Summer RSVP, with Code: SUMMER15

Description

black pearl asiatic lily LilyPRESALE Spring 2026 Shipping Schedule: 2 27 2026 Lilies are easy to grow and provide great summer color for your landscape, containers, garden beds and borders. Lilies are great for cut flower arrangements and bouquets and attract hummingbirds to your garden! Lilies are among the most stately of all bulbous plants. They are winter hardy in most areas and will return and bloom for many years. Lilies are ideal for planting under trees and among shrubs.

PRESALE

Spring 2026 Shipping Schedule: 2/27/2026

Lilies are easy to grow and provide great summer color for your landscape, containers, garden beds and borders. Lilies are great for cut flower arrangements and bouquets and attract hummingbirds to your garden! Lilies are among the most stately of all bulbous plants. They are winter hardy in most areas and will return and bloom for many years. Lilies are ideal for planting under trees and among shrubs.

About this Variety

While there are no 100% true black flowers available at the present time, this Asiatic variety comes very close. Lily Black Charm is sought after for its deep, blackish color.

Highlights

  • Returns and blooms year after year in most hardy zones
  • Perfect for garden beds and borders
  • Beautiful, dramatic blooms
  • Excellent for bouquets and cut flower arrangements
  • Easy to grow

Exposure:

Full Sun to Partial Shade

Blooms:

Summer

Height:

Grows 36-48" tall

Spacing/Depth:

Plant 4-6" apart, 4-6" deep

USDA Zones:

Grow as Perennial in zones 4-9. Grow as Annual elsewhere.

Growing Instructions

Lilies like cool roots and should be grown in partial or filtered sun. Lilies prefer to be planted in moist yet well draining soil. Soil preparation: The ideal soil is porous and drains well and yet still holds enough water for the roots. If your soil is heavy clay and drains slowly, or very sandy and does not hold water, add organic soil amendments (peat moss or ground bark). Spread a 3-4" layer of soil amendment, add fertilizer and incorporate together into the soil to a depth of 10". To plant: Dig a hole 4-6" deep in a full sun to partially shaded area, in a well drained location. Place the lily bulbs pointed ends up and space 4-6" apart. Cover with soil. Water thoroughly at planting. While actively growing, water frequently and thoroughly so that the water will reach the roots. Mulch to keep the soil cool and to prevent the soil from drying out. Do not allow the roots to dry out. Mass planting: When planting a large quantity of lily bulbs in one bed, excavate the entire area to 3-6" deep, work fertilizer into the bottom of the trench, set out the bulbs and then cover the bulbs all at once. Apply fertilizer before and after flowering. Cover with frost protective material during the winter months.

Care Tip

Plant lilies as soon as possible after you purchase them. If you must wait, your lily bulbs should be refrigerated to slow their growth. When cutting the flowers for bouquets, remove no more than one-third of the stem so that enough leaves remain to renew the bulb for the coming year.

Shipping Notes
  • Free Standard Shipping on $100+ Orders to the USA.
  • Except Preorder products are shipped in 48 hours.
  • Delivery to the USA:
  1. Standard Shipping : 3-10 business days
  • If time is of the essence, please consider selecting expedited delivery for faster service.
Exchange/Return Notes
  • We offer a 30-day return/exchange service after receiving.
  • Final sale items are not eligible for returns or exchanges.
  • To process your return/exchange, please contact us at [email protected]
  • Please click here for more details>>> Return & Exchange Policy
SKU: 59094985843

Discover Niche Categories That Outsell black pearl asiatic lily

Top-Converting Item to Boost Your Average Order

4.4 ★★★★★
Based on 29 reviews
Sort
Highest Rating
Newest First
Oldest First
Product Reviews
X
Verified Purchase
0x00000000:00000000
Houston, 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.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on April 18, 2017
Z
Verified Purchase
Zygerian99
Belleville, 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.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on January 21, 2020
S
Verified Purchase
Shannon
Grantham, 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!!
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on November 30, 2025
W
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.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on March 15, 2017
A
Verified Purchase
Adam
Dallas, 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

recommand products

Canary Island Date Palm, Phoenix Canariensis, Pineapple Palm
27.64
Crassula ovata 'Jade Crosby' – DesertScapeSucculent
27.72
Jade Plant - Friendship Plant
29.75
Crassula Ovata Jade Crosby
21.26
Palm Rhapis Excelsa – ShopTropicals
29.39