SKU: 77336344475
darklord philodendron

darklord philodendron Philodendron erubescens × 'Dark Lord' – Dramatic Dark Hybrid

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Description

darklord philodendron Philodendron erubescens × 'Dark Lord' – Dramatic Dark HybridPhilodendron erubescens x 'Dark Lord' Bold Contrast and Hybrid Growth Tips Philodendron erubescens x 'Dark Lord' is a striking hybrid cultivar with large, glossy leaves that shift in tone from deep burgundy to near black, framed by bright red petioles and undersides. New growth emerges in coppery shades, then deepens dramatically as leaves mature creating a layered, moody aesthetic that lives up to the name. This is a vigorous climbing hybrid with

Philodendron erubescens x 'Dark Lord' – Bold Contrast and Hybrid Growth Tips

Philodendron erubescens x 'Dark Lord' is a striking hybrid cultivar with large, glossy leaves that shift in tone from deep burgundy to near-black, framed by bright red petioles and undersides. New growth emerges in coppery shades, then deepens dramatically as leaves mature – creating a layered, moody aesthetic that lives up to the name. This is a vigorous climbing hybrid with strong stems and wide-spreading foliage, perfect for users looking to grow something bold and visually intense. With proper support, it can reach impressive size indoors while maintaining manageable care needs.

Foliage Details and Growth Habit of Philodendron 'Dark Lord'

  • Leaf shape: Broad, heart-shaped to arrowhead with prominent veining
  • Colour: Ranges from coppery-orange to deep green and black-maroon
  • Petioles: Reddish to bright crimson, creating contrast against the foliage
  • Growth habit: Climbing; benefits from a moss pole or vertical support
  • Indoor size: Can grow over 100 cm tall and wide with space and support

Hybrid Background of Philodendron erubescens 'Dark Lord'

Philodendron erubescens is native to Colombia, where it grows in humid lowland forests as a hemiepiphytic climber. This hybrid cultivar – 'Dark Lord' – has been selected for its intense pigmentation and faster growth. It adapts well to indoor conditions if given enough warmth, humidity, and light. Temperatures between 22 – 28 °C and bright, indirect light are ideal. While dramatic in appearance, it's surprisingly easy to manage, making it a collector's favourite.

Philodendron 'Dark Lord' is toxic if ingested – keep out of reach of pets and children.

Care Instructions for Philodendron 'Dark Lord'

  • Light: Bright, indirect light keeps foliage dark and vibrant; avoid direct sun
  • Watering: Water when top 3 – 4 cm of soil is dry; do not allow to sit in water
  • Humidity: Thrives in 60 – 80% humidity; tolerates lower but grows slower
  • Temperature: Keep between 20 – 28 °C; protect from drafts and dry heat
  • Soil: Chunky, well-draining aroid mix with bark, perlite, and compost
  • Repotting: Every 1 – 2 years or when roots outgrow the container
  • Fertilising: Feed monthly with a balanced houseplant fertiliser
  • Propagation: Via stem cuttings with at least one node and aerial root
  • Hydroponics: Can adapt to semi-hydro systems in controlled humidity
  • Pruning: Cut back leggy vines or lower leaves to maintain shape

Typical Issues with Philodendron 'Dark Lord'

  • Leaf fading: May occur in low light – move to brighter spot with indirect light
  • Yellowing leaves: Often caused by overwatering or poor drainage
  • Dry tips or edges: Usually a humidity issue – consider grouping with other plants
  • Thrips or spider mites: Can appear on new leaves – treat with neem or insecticidal soap
  • Fungus gnats: Let soil surface dry between watering and improve airflow

Additional Notes for Growing Philodendron 'Dark Lord'

This cultivar grows fast and confidently in the right conditions, especially when given vertical space to climb. To maintain compact internodes and the richest colour, grow 'Dark Lord' in a bright, filtered location with consistent warmth and moderate humidity. Its large leaves can benefit from occasional wiping to stay glossy and healthy. Mimicking rainforest-like light and airflow keeps it vigorous and visually intense year-round.

Etymology and Botanical Naming of Philodendron erubescens 'Dark Lord'

Philodendron comes from Greek “philo” (love) and “dendron” (tree), referring to its climbing habit. The species name erubescens means “reddening” in Latin, referencing the red stems and petioles. 'Dark Lord' is a cultivar name chosen for the plant’s dark, almost supernatural leaf tones and bold presence.

Buy Philodendron 'Dark Lord' Now

Make a statement with bold, moody foliage and rich colour contrast – Philodendron 'Dark Lord' is a hybrid with serious impact and surprising ease. Order now and grow your own tropical powerhouse.

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Carol
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Need to read book
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The truth about the Native people. THANK YOU Kent for writing this book. We purchased about 12 total.
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Walter Echo-Hawk, author of THE SEA OF GRASS.
Belleville, US
★★★★★ 5
Native American history at its best!
Format: Hardcover
Kent Blansett's engrossing story about the life & times of the famed Mohawk activist Richard Oakes is Native American history at its best. I appreciated the well-written context provided about the birth, growth and impact of the Red Power Movement and the pivotal role that social justice activism played in the rise of modern Indian nations in the United States today. This scholarly work helps us understand modern Native America and is a "must-read" for every Native American Studies student and scholar, as well as readers interested in important American social justice movements.
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Reviewed in the United States on April 1, 2019
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Chelsea, US
★★★★★ 5
Excellent book on ML
Format: Paperback
This is a great book on machine learning. Topics covered are extensive - from beginner level to advanced topics including math behind different algorithms. However, not "all" algorithms are covered. Please go through the table of contents. The first part - 11 chapters - covers machine learning concepts and second part covers advanced topics with Pytorch. There are lots of excellent code and they work!! The quality of the book I received is excellent. I have gone through all 742 pages, and it has held up very well!! I used Jupyter notebook to run all examples. I created a new notebook and copied and pasted the code and ran them. This approach worked very well for me. At the same time, I could experiment with my take on the code snippets and definitely added to my knowledge. Only issue I have is on the second part of the book discussing PyTorch: (1) Some packages are a bit older version: e.g., transformer 4.9.1 whereas current version is 4.48+. It took some tweaking/recoding to get the examples working. (2) There is not much discussion on why certain architecture was chosen - e.g., number of layers, is there a rule of thumb on how to improve performance by changing these parameters? Even with CUDA the code run for a long time. Therefore, experimenting with different values of parameters become too time consuming. (3) On the same note, if I can achieve test accuracy of 90%+ using logistic regression and almost the same (perhaps one or two percent better with PyTorch with IMDB movie review dataset and that two much faster why should I use PyTorch for this dataset? Obviously, PyTorch is for certain types of problems. Discussions can be included by not adding to the exhaustive (and apt) contents. Personally I was disappointed by lack of any example on time series. Must have for ML practitioner as a reference and guide.
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Reviewed in the United States on December 20, 2024
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Richard Hackathorn
West Palm Beach, US
★★★★★ 5
Excellent Textbook for Hands-On Learning of ML
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This textbook is for the serious life-long learners of machine learning. There are at least two ways to ‘consume’ this book. For the expert in ML, this is a textbook to study as a clear comprehensive ML overview and then to dive into sections of interest or ignorance. The concepts are grounded in code examples and are well cited (with links) to sources. Further, this textbook is appropriate if you are TensorFlow-centric and want to broaden into cutting-edge ML models/tools coded in PyTorch. For a new learner to ML, this is a textbook to DO (not just READ) with hands-on and brain-engaged. If you realize that ML is a key life-long skill for your career, consider this textbook as part of a daily learning habit (10-30 min). From personal experience, my advice to the new learner is as follows… First, clone the GitHub repository, setup your Python environment, and study the textbook, while working through the notebooks. Go on tangents and break the code. Do this methodically as part of your daily learning habit, but do not hesitate to jump ahead several chapters to prepare for tomorrow’s meeting. There is enough excellent material here for a full year of ML adventures. I did a similar strategy with Raschka’s first textbook. About four years ago, I had finished Andrew Ng’s Deep Learning Specialization as a student in his first cohort. I knew the concepts well but could not do the actual application coding. I was surprised how my Python coding improved by following Raschka’s clean and elegant style. And Raschka’s code examples were meaty enough to be springboards into working applications. Several textbook editions later, what is different about this new edition? First, it moves you through scikit-Learn (a firm foundation) to PyTorch, instead of TensorFlow. PyTorch is a better stepping-stone, both conceptually and practically. With PyTorch, you will go further with less energy, while being able to convert your efforts into TensorFlow as needed. In addition, most of the cutting-edge ML/AI/DL research is in PyTorch. It is nice to read a recent arXiv paper, clone their repository, click on the Colab tutorial, and replicate their experiments, along with picking up a ton of new coding tricks & tips. I am excited to work through these PyTorch sections to hone my skills. Second, there is a clear recognition of model tracking and tuning practices. This is often a gap in other ML textbooks and courses. Once you progress beyond the simple demo examples in a lecture, you realize that the real work is experiments, more experiments, and still more experiments, so that you must understand what the model architecture and hyperparameters are doing to your dataset. There is good coverage of scikit-Learn pipeline, grid search, model performance, and the like. Third, ML/AI/DL practice is rapidly evolving. Every week new ML packages/services become available that could save much grief on your current project. What is refreshing about Raschka’s textbook series is that he constantly adding cutting-edge topics because he likes to stay current and to help us stay current. Hence, this edition contains recent ML treats as: transformers, self-supervised learning, autoencoders-to-GAN, graph neural networks, DBSCAN, t-SNE (with brief mention of UMAP), and PyTorch-Lightning.
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Reviewed in the United States on February 26, 2022
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Amazon Customer
Massapequa, US
★★★★★ 4
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Format: Paperback
Nice learning book just have to finish it
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