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dracaena reflexa 中文 造型百合竹｜Styled Dracaena Reflexa – Jungle CornerDracaena reflexa 1. 2. 3. 18C24C 4. 5. : 150 160cm : 60 70cm : : : 18C 24C
百合竹（Dracaena reflexa）是一種常見的室內綠色植物。它具有彎曲的莖幹和綠色的葉子，葉子通常呈現橢圓形或狹長形。
百合竹是熱帶和亞熱帶地區原產的植物，在室內環境中可以作為觀葉植物栽培。
養護要點：
1. 光照：百合竹偏好明亮但避免強烈陽光直射的環境。最好將其放在室內明亮的位置，避免長時間暴露在炎熱的陽光下，以免葉子受傷。
2. 水分：保持土壤濕潤但不要過度澆水。在每次澆水之間，讓土壤表層稍微乾燥。過度澆水可能導致根部腐爛。使用適量的水，使土壤保持適當的濕潤度。
3. 溫度：百合竹適應溫暖的環境，理想的生長溫度約在18°C至24°C之間。避免將其置於極端寒冷或極端炎熱的環境中。
4. 容器和土壤：使用排水良好的養殖容器和富含有機質的土壤。確保養殖容器底部有良好的排水孔，以防止過度積水。
5. 裝飾和擺放：百合竹可以作為室內裝飾植物，放置在辦公室、客廳或臥室等地方。它的彎曲莖幹和綠色葉子給人帶來視覺上的舒適感。可以根據個人喜好選擇合適的容器和擺放方式。
產品規格
高度: 150-160cm
闊度: 60-70cm
光照: ◆ ◆ ◇ ◇
水份: ◆ ◆ ◇ ◇
溫度: 18°C-24°C
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4.9 ★★★★★
Based on 20 reviews
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Product Reviews
★★★★★ 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
★★★★★ 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.
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Reviewed in the United States on May 22, 2026
★★★★★ 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
★★★★★ 1
A rushed, poorly written guide of how the "experts" can't really explain what Deep Learning is
Format: Hardcover
This book, in every sense of the word, is rushed. I think the authors wanted to establish themselves as leaders of this young-ish field, but does so by sacrificing quality. It also shows that Deep Learning theory has been there for a long time, known by another name called Neural Networks. The interesting algorithms are of MLP, Back Propagation and the classical neural networks. The optimization methods such as Adam are the ones that are new and interesting, and the only ones worthy of in this book. So, essentially, what you get from this book is use A for X, B for Y and C for Z type of dry, un-intuitive, badly written waste of paper.
As for the structure of the book, it's like an example of how not to structure a book. It has some linear algebra, probability at the start (not good enough, and confuses more people and wastes paper). Goes on to prove other algorithms such as PCA (yeah, ok!). Then, talks about how this architecture works for this and that architecture.
So, yeah, if you really want to try out deep learning, don't buy this book. Set up Tensorflow/pytorch/ other library, run the tutorials, find an architecture for the problem you are interested in and start tweaking that. You will have far more fun and would have saved your money.
The praise that this book gets is beyond me. Did Musk even read this book? I doubt it.
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Reviewed in the United States on May 15, 2018