SKU: 20883078559
red rosette succulent

red rosette succulent Shop 'Echeveria minima' Care and Growing Guide

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red rosette succulent Shop 'Echeveria minima' Care and Growing GuideIntroducing the Echeveria minima, also known as the Miniature Echeveria or Miniature Ghost Echeveria, which is a small, compact succulent plant that belongs to the Echeveria, opens in a new tabGo to collections echeveria succulent genus. It is a popular choice among succulent enthusiasts because of its charming appearance and ease of care. Native to Mexico, where this beautiful succulent can be found growing in rocky areas and desert regions. This

Introducing the Echeveria minima, also known as the Miniature Echeveria or Miniature Ghost Echeveria, which is a small, compact succulent plant that belongs to the Echeveria, opens in a new tabGo to collections echeveria succulent genus. It is a popular choice among succulent enthusiasts because of its charming appearance and ease of care. 

Native to Mexico, where this beautiful succulent can be found growing in rocky areas and desert regions. This minima Echeveria thrives in arid climates and has adapted to survive in dry conditions. 


The Echeveria minima is a petite succulent, growing to a height of about 4 inches and spreading up to 6 inches in diameter.

It forms tight rosettes of thick, fleshy leaves that are usually bluish-gray or silvery-green in color.

This frosty mint rosette’s leaves have well-defined points and edges tinged with pink.

The leaves are packed so densely that only the upper halves are visible, creating a tight, mounding cluster that emphasizes its compact form.

As the rosettes grow and produce small offsets. The leaves are spoon-shaped and have a powdery coating called “farina” that gives them a ghostly appearance. The Echeveria minima flowers bloom during the late spring and summer months, with slender, arching flower stalks that can reach up to 8 inches in height. Its pink and yellow-bell-shaped flowers add a delightful touch of color to the plant and attract pollinators like bees and butterflies. 

When it comes to Echeveria minima care, it prefers a well-draining soil mix specifically formulated for succulents. Water the plant thoroughly, allowing the soil to dry out completely between waterings to prevent rot. Additionally, avoid overwatering as this can lead to root rot in these succulents. Removing dead leaves from the bottom of the plant is crucial to prevent rot and fungal diseases. 

When growing indoors, your Echeveria minima can thrive in average room temperatures, ranging from 65°F to 75°F. It can tolerate slightly cooler temperatures down to 50°F but should be protected from frost and extreme cold. It requires bright but indirect sunlight indoors. It’s important to avoid placing the plant near cold drafts or air conditioning vents, as sudden temperature changes can stress the plant. 

For outdoor cultivation, Echeveria minima is suitable for USDA hardiness zones 9 to 11. These zones generally have mild winters and warm to hot summers. However, Echeveria minima is not cold hardy enough to withstand freezing temperatures without protection. It prefers full sun to partial shade outdoors for at least 4-6 hours per day. 

If you live in a colder climate, you can still enjoy Echeveria minima, a potted plant that you can bring indoors during the winter months to protect it from freezing temperatures. The minima Echeveria can be propagated through various methods. One common way is by taking stem cuttings from the mother plant. Simply cut a healthy stem, give it a few days to callus, and then plant it in well-drained soil.

Another method is by using leaf cuttings. Gently remove a leaf from the plant, let it dry out for a few days, and then place it on top of the soil. With time, new roots and baby plants will emerge. Echeveria minima or Miniature Echeveria is an attractive addition to any room. This succulent is a great choice for any beginner or experienced succulent gardener! 

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Zygerian99
Alexandria, 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
Draper, 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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William P Ross
New York, 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
Charlottesville, 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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Amazon Customer
Pawtucket, US
★★★★★ 5
Comprehensive! The Bible of Deep Learning!
This book has by far surpassed my expectations! I have purchased many machine learning and deep neural network books in the past, but nothing has ever come close to this book! First of all, it is written by the fathers of Deep Learning, and is therefore an authority. Secondly, the book is broken into three parts: 1. A math overview and refresher. 2. Deep Learning applications and 3. Research in Deep Learning. I can't help but go through this book from front to back. It is a smooth read, and every sentence written is meaningful. These guys know their stuff! And after you read this book, YOU WILL ALSO know your stuff! If you feel daunted by the price, just remember, you get what you pay for! I'd say they could easily charge about $300+ for this book, but they are doing everyone a very kind favor by ONLY charging this reasonable amount. You get A LOT of bang for your buck with this purchase. I hesitated at first about buying this book because of the price, but I am soooooo happy that I did! Worth every penny! Look no further, get this book and start your Deep Learning journey!!
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Reviewed in the United States on July 14, 2017

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