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Description
neon pothos or lemon lime philodendron Philodendron cordatum 'Lemon' (Low Light Tolerant) 6” potVibrant Chartreuse Foliage for Easy Indoor Dcor A cultivar of the classic heartleaf philodendron, the Lemon Lime (Philodendron hederaceum) is a fast growing, cascading houseplant prized for its vivid chartreuse foliage. New leaves emerge an electric pinkish yellow, mature to deep lemon, and finally develop into a lively lime green. Renowned for its adaptability and minimal care needs, the Lemon Lime Philodendron can thrive in a variety of indoor
Vibrant Chartreuse Foliage for Easy Indoor Décor
A cultivar of the classic heartleaf philodendron, the ‘Lemon Lime’ (Philodendron hederaceum) is a fast-growing, cascading houseplant prized for its vivid chartreuse foliage. New leaves emerge an electric pinkish-yellow, mature to deep lemon, and finally develop into a lively lime green. Renowned for its adaptability and minimal care needs, the Lemon Lime Philodendron can thrive in a variety of indoor environments—perfect for seasoned plant parents or beginners seeking a splash of color.
Plant Highlights
- Striking Foliage: Leaves transition from pinkish-yellow to lemon, then lime green.
- Easy-Care Houseplant: Requires minimal attention and adapts to diverse light conditions.
- Rapid Growth: Trails gracefully, making it ideal for hanging baskets or shelves.
- Versatile Décor Piece: Use as a standalone focal point or mix with other houseplants.
Plant Profile
- Botanical Name: Philodendron hederaceum
- Common Names: Lemon Lime Philodendron, Philo Lemon Lime, Heartleaf Philodendron
- Family: Araceae
- Native Range: Central & South America
Care Guide
- Care Level: Easy
- Light: Tolerates low to bright indirect light
- Water: Keep soil evenly moist in spring and summer; allow slight drying between waterings in fall and winter
- Humidity: Prefers moderate (50%+)
- Temperature: Thrives between 60–80°F
- Pruning: Trim to remove brown or dead leaves and control length
- Fertilizer: Apply a balanced feed once monthly in spring and summer
- Propagation: Propagate easily via stem tip cuttings in water or soil.
- Growth: Trails up to 12 ft. Repot every 1–2 years as needed.
- Pests: Generally pest-free.
- Toxicity: Toxic to humans and pets if ingested.
Fun Fact: Color-Changing Leaves
The Lemon Lime Philodendron is famous for its dynamic leaf colors. Each new leaf opens with a vivid pinkish-yellow shade before transitioning through deep lemon and finally settling into a vibrant lime green—creating a show-stopping display throughout the year.
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4.2 ★★★★★
Based on 2038 reviews
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Product Reviews
★★★★★ 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!!
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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