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are heart leaf philodendron toxic to cats

are heart leaf philodendron toxic to cats Heart Leaf Philodendron "Brasil" (Hederaceum)

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Description

are heart leaf philodendron toxic to cats Heart Leaf Philodendron "Brasil" (Hederaceum)Brasil Heart Leaf Philodendron (Philodendron hederaceum 'Brasil') is a popular, low maintenance houseplant with heart shaped leaves featuring a striking yellow green variegation. This easy to grow plant adds a touch of tropical beauty to your indoor space. Follow these care instructions to keep your Brasil Heart Leaf Philodendron healthy and thriving: Light: Place your Brasil Heart Leaf Philodendron in a location with bright, indirect light. While it

Brasil Heart Leaf Philodendron (Philodendron hederaceum 'Brasil') is a popular, low-maintenance houseplant with heart-shaped leaves featuring a striking yellow-green variegation. This easy-to-grow plant adds a touch of tropical beauty to your indoor space. Follow these care instructions to keep your Brasil Heart Leaf Philodendron healthy and thriving:

  1. Light: Place your Brasil Heart Leaf Philodendron in a location with bright, indirect light. While it can tolerate low light conditions, the variegation on the leaves may become less pronounced. Avoid direct sunlight, as it can scorch the leaves.

  2. Water: Water your philodendron when the top 1-2 inches of soil feels dry to the touch. Water thoroughly until it drains from the bottom of the pot, and then allow the excess water to drain completely. Avoid overwatering, as this can lead to root rot. This plant prefers slightly moist soil but can tolerate short periods of dryness.

  3. Humidity: Brasil Heart Leaf Philodendron thrives in moderate to high humidity levels. It can tolerate average indoor humidity, but it will appreciate a boost, especially during dry winter months. To increase humidity, you can place a tray filled with water and pebbles underneath the pot, mist the leaves occasionally, or use a humidifier.

  4. Temperature: Maintain a temperature range of 65-85°F (18-29°C) for optimal growth. This plant can tolerate a range of temperatures but avoid exposing it to temperatures below 55°F (13°C) or sudden temperature changes.

  5. Fertilizer: Feed your Brasil Heart Leaf Philodendron with a balanced liquid fertilizer every 4-6 weeks during the growing season (spring and summer). Use a half-strength solution to avoid over-fertilizing. Reduce feeding to once every 2-3 months during fall and winter.

  6. Pruning: Prune your philodendron as needed to maintain its shape and size. Remove any yellow or brown leaves and trim the tips if they become damaged. You can also pinch back the growing tips to encourage bushier growth.

  7. Repotting: Repot your Brasil Heart Leaf Philodendron every 2-3 years or when the roots outgrow the pot. Choose a well-draining potting mix and a pot that is 1-2 inches larger in diameter than the current one. Be gentle when handling the roots, and water the plant thoroughly after repotting.

  8. Pest control: Keep an eye out for common pests such as spider mites, mealybugs, and scale insects. Treat any infestations with insecticidal soap or neem oil, following the manufacturer's instructions.

Pet-friendly or toxic to pets?

According to the ASPCA, the Heart Leaf Philodendron, including the Brasil variety, is toxic to cats and dogs. If ingested, it can cause symptoms such as oral irritation, swelling of the mouth, tongue, and lips, excessive drooling, vomiting, and difficulty swallowing due to the presence of insoluble calcium oxalates. It's best to keep your Brasil Heart Leaf Philodendron out of reach of pets to prevent any potential health issues. If you suspect your pet has ingested any part of the plant, contact your veterinarian immediately.

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Walter Echo-Hawk, author of THE SEA OF GRASS.
Lowell, 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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Carnegie, 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
Massapequa, US
★★★★★ 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
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Amazon Customer
Houston, US
★★★★★ 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
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Kindle Customer
Belleville, US
★★★★★ 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

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