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golden pothos clipping Golden Pothos Plant Node Cuttings, Common Popluar Houseplant and tropi – Rooting 4 JoyGolden Pothos Plant Info & Care Guide Botanical Name: Epipremnum aureumCommon Names: Golden Pothos, Devils Ivy, Money PlantFamily: AraceaeType: Tropical trailing or climbing houseplantOrigin: Southeast Asia Overview The Golden Pothos is one of the most popular and dependable houseplants around and for good reason! Its beautiful heart shaped green leaves streaked with golden yellow variegation make it a vibrant addition to any indoor space. It's
🌿 Golden Pothos – Plant Info & Care Guide
Botanical Name: Epipremnum aureum
Common Names: Golden Pothos, Devil’s Ivy, Money Plant
Family: Araceae
Type: Tropical trailing or climbing houseplant
Origin: Southeast Asia
✨ Overview
The Golden Pothos is one of the most popular and dependable houseplants around — and for good reason! Its beautiful heart-shaped green leaves streaked with golden-yellow variegation make it a vibrant addition to any indoor space. It's incredibly adaptable, easy to care for, and known for thriving in a wide range of conditions.
Whether trailing from a hanging basket or climbing up a moss pole, Golden Pothos grows quickly and can add a lush, tropical vibe to any home, office, or dorm.
💡 Light Requirements
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Grows best in bright, indirect light
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Tolerates low light but may lose some variegation
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Avoid direct sunlight, which can scorch the leaves
💧 Watering
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Water when the top 1–2 inches of soil feel dry
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Avoid overwatering; ensure good drainage
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In winter, water less frequently
🌡️ Temperature & Humidity
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Ideal range: 65–85°F (18–29°C)
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Keep away from drafts and temperatures below 55°F (13°C)
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Thrives in average indoor humidity
🌱 Soil & Potting
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Prefers a well-draining potting mix
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Repot every 1–2 years or when root-bound
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Works well in hanging baskets, planters, or climbing supports
🍃 Fertilizing
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Feed once a month during spring and summer with a balanced liquid fertilizer
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No fertilizer needed in winter
✂️ Pruning & Training
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Prune to encourage bushier growth or control length
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Can be trained to climb or trail
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Great for shelves, trellises, or macramé hangers
🌿 Propagation
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Very easy to propagate via stem cuttings in water or soil
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Each cutting should include at least one node
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Roots typically develop in 2–4 weeks
❗ Common Issues
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Yellowing leaves: Often caused by overwatering
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Faded variegation: May need more light
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Leggy growth: Could be a sign of low light
🧡 Why You'll Love It
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Low maintenance and beginner-friendly
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Fast growing and super adaptable
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Helps clean indoor air
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Looks amazing trailing or climbing
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A timeless classic for any plant collection
Node Stem Cutting or Small Rooted Starter Plant
Each plant cutting you receive will include one node and one leaf maybe more. All cuttings are freshly cut with a stem and node to help with propagation. The node is the point where the leaf attaches to the stem, and it will often look like a small "bump" on plants like pothos, monstera, or philodendron. Keep in mind, aerial roots may or may not be present at the time of clipping so you might not notice the node but each cutting will have at least one node and one leaf ready for water or soil propagation!
‼️Important: After purchase, please read the note from the me for tips and tricks on propagating. While it’s rare, sometimes cuttings don’t survive the shipping process. If you notice any issues, let me know within 48 hours of receiving your order and I’ll gladly offer a refund or replacement or coupon. However, it's normal for plants to show signs of stress like wilting, yellowing, or browning during shipping, but the nodes should still be viable. If there’s a problem with your order, please contact me before leaving a review so I can help resolve it. I can only assist if you reach out within 48 hours of delivery!
If temperatures in your area are expected to drop below 50°F for a few days, we recommend adding a heat pack to your order. You can easily purchase one in our shop or select it as a shipping upgrade at checkout. This will help keep your plant cutting safe during shipping and reduce the chance of any issues.
All cuttings are shipped via USPS standard shipping. If you'd like a faster option, please choose priority shipping at checkout.
Lastly, please keep an eye on the weather in your area, as I’m unable to control any weather-related issues, shipping delays, or errors made by USPS. If you have concerns about delivery, please contact USPS directly, but I’m here to help in any way I can!
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- Free Standard Shipping on $100+ Orders to the USA.
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Exchange/Return Notes
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4.4 ★★★★★
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Product Reviews
★★★★★ 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.
WAS THIS REVIEW HELPFUL?YesReportShare
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
★★★★★ 5
Very useful book
Format: Paperback
I use it for the machine learning class I teach.
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Reviewed in the United States on May 3, 2026