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Description
philodendron montanum Philodendron pastazanum – Foliage FactoryPhilodendron pastazanum Philodendron pastazanum is a ground creeping species in the Araceae family, with broad green leaves, long petioles and thick stems that move across the substrate surface. Mature leaves become large and broadly ovate to peltate, with visible venation and a wide blade held above the creeping stem. The repent stem travels over the substrate and needs surface room as new leaves develop. A wide pot allows the plant to extend
Philodendron pastazanum
Philodendron pastazanum is a ground-creeping species in the Araceae family, with broad green leaves, long petioles and thick stems that move across the substrate surface. Mature leaves become large and broadly ovate to peltate, with visible venation and a wide blade held above the creeping stem.
The repent stem travels over the substrate and needs surface room as new leaves develop. A wide pot allows the plant to extend naturally while keeping the growth point above the mix.
- Leaf shape: Broadly ovate to peltate green blades with a wide base.
- Venation: Strong basal and lateral veins become clearer as leaves size up.
- Growth habit: Thick creeping to repent stem that travels across the substrate surface.
- Native range: Ecuador to Peru, associated with wet tropical forest.
Philodendron pastazanum care for creeping growth
Use a wide, stable pot for Philodendron pastazanum and keep the creeping stem visible above the mix. The roots need moisture and oxygen at the same time, so a chunky aroid substrate is safer than a dense, water-retentive potting soil.
- Pot choice: A wide pot gives the creeping stem space to extend without being buried.
- Light: Bright indirect light, with protection from direct sun that can mark the broad leaf surface.
- Water: Water when the upper substrate has started to dry, then let the pot drain fully.
- Humidity: Moderate to higher humidity helps large leaves expand with fewer dry edges.
- Temperature: Keep warm and stable, ideally above 18 °C, and protect the root zone from cold wet conditions.
- Substrate: Use a coarse aroid mix with bark, mineral structure and fibre around the roots.
- Repotting: Repot when the creeping stem reaches the pot edge, roots fill the container, or the substrate starts to break down.
- Fertilising: Feed lightly during active growth with a balanced houseplant fertiliser at diluted strength.
- Growth rate: Growth is usually steady indoors, with larger leaves developing when the stem has room to travel.
Philodendron pastazanum pruning, propagation and mineral substrates
- Pruning: Remove damaged leaves close to the petiole base and keep the creeping stem above the substrate surface.
- Propagation: Propagate from stem sections with at least one node and healthy root or aerial-root tissue.
- Semi-hydro: Suitable for mineral or semi-hydro substrates if the rhizome-like stem stays above the medium and the roots remain oxygenated.
- Training: Guide the stem across the pot surface rather than burying it into the mix.
Philodendron pastazanum stem, roots and broad-leaf issues
- Soft creeping stem: Check whether the stem is buried or sitting against wet, compact substrate.
- Yellowing leaves: Inspect roots if the mix stays wet deep in the pot.
- Damaged leaf edges: Large leaves can mark from dry air, irregular watering or physical contact.
- Root issues: Slow growth with yellowing leaves often points to poor drainage, compacted substrate or cold wet roots.
- Pests: Check the leaf undersides, petioles and new growth for spider mites, thrips, mealybugs and scale.
Philodendron pastazanum safety and botanical background
Keep Philodendron pastazanum away from pets and children because its tissues contain calcium oxalate crystals. Sap may irritate sensitive skin.
Heinrich Wilhelm Schott published Philodendron in Wiener Zeitschrift für Kunst 3: 780 in 1829; the genus name comes from Greek-derived roots meaning “tree-loving.” Philodendron pastazanum was described by Kurt Krause in Notizblatt des Botanischen Gartens und Museums zu Berlin-Dahlem 15: 42 in 1940. The epithet pastazanum is formed from Pastaza with the Latinized ending -anum, meaning “of” or “from” Pastaza.
In a wide container, mature Philodendron pastazanum develops broad peltate leaves above a thick creeping stem that stays visible at the surface.
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4.1 ★★★★★
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Product Reviews
★★★★★ 5
Need to read book
Format: Hardcover
The truth about the Native people.
THANK YOU Kent for writing this book.
We purchased about 12 total.
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Reviewed in the United States on November 24, 2019
★★★★★ 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
★★★★★ 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
★★★★★ 5
Excellent Textbook for Hands-On Learning of ML
Format: Kindle
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
★★★★★ 4
Just learning it
Format: Paperback
Nice learning book just have to finish it
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Reviewed in the United States on December 10, 2025