SKU: 4380928010
decorative grow lights indoor plants

decorative grow lights indoor plants USB Grow Light | Decorative Grow Lights for Indoor Plants | Dimmable Grow Light Bulb

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Description

decorative grow lights indoor plants USB Grow Light | Decorative Grow Lights for Indoor Plants | Dimmable Grow Light BulbBring the outdoors in with our awesome LED Grow Light Track! This small led grow light is perfect for keeping your houseplants thriving all year round. Shaped like a mini running track, this compact grow light is ideal for smaller indoor plants. Its modern design looks great in any space, blending right in with your home decor. Powered by a simple USB connection, the 6W LED grow light provides full spectrum lighting to give your plants exactly what

Bring the outdoors in with our awesome LED Grow Light Track! This small led grow light is perfect for keeping your houseplants thriving all year round.

Shaped like a mini running track, this compact grow light is ideal for smaller indoor plants. Its modern design looks great in any space, blending right in with your home decor.

Powered by a simple USB connection, the 6W LED grow light provides full-spectrum lighting to give your plants exactly what they need to grow strong and healthy. Choose between a warm 4000K yellow light, perfect for most houseplants and herbs, or a pinkish white light that's awesome for succulents, seedlings, and carnivorous plants like Venus flytraps. With 50 bright LED bulbs, your plants will be soaking up all the good stuff!

Versatility is key with this grow light. Pick a 1-head, 2-head, or 3-head option, and you can even connect multiple units together for wider coverage. Nifty trays underneath let you place your plant babies right under the lights for optimum growth.

Setting it up is a breeze too! The built-in timer lets you pick 8, 12, or 16 hours of light per day on a 24-hour cycle. An on/off switch gives you full control. And with a 1.2m wire, you can position the light wherever your plants need it most.

Don't let your indoor plants struggle with lack of sunlight! Give them the light they crave with our LED Grow Light Track. Upgrade your plant game and see those green babies thrive!

Grab your LED Grow Light Track today and turn your home into a lush, plant-filled oasis!


 Specifications

Feature Description
Lighting LED
Material Zinc Alloy & PC
LED Amount 50pcs/head
Spectrum Full Spectrum
Single Head Power 6W
Head Size 260*90mm
Plug USB
Rings 1 / 2 / 3
LED Quantity 50 / 100 / 150
Power 6W / 12W / 18W
Input Current 5V2A / 5V3A / 5V5A
Lighting Range 35cm in length, 20cm in width
Suitable Distance From Lamp to Plant 5-20 cm
Switch 8h / 12h / 16h timing, 24h cycle, on/off
Life Time 50000h
Wire Length 1.2m
Colors Pinkish White / Sunshine Yellow
Package Size Single and Double Heads 31*13*3cm, Three Heads 34*13*5cm
Weight Single Head 354g, Double Heads 528g, Three Heads 736g

 

Spectrum

 

 

 

Dimension


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SKU: 4380928010

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4.1 ★★★★★
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Product Reviews
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Walter Echo-Hawk, author of THE SEA OF GRASS.
Chelsea, 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.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on April 1, 2019
P
Verified Purchase
Par
Belleville, 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.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on December 20, 2024
R
Verified Purchase
Richard Hackathorn
Lake Worth, 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
A
Verified Purchase
Amazon Customer
Natrona Heights, US
★★★★★ 4
Just learning it
Format: Paperback
Nice learning book just have to finish it
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on December 10, 2025
K
Verified Purchase
Kindle Customer
Belleville, US
★★★★★ 5
Very useful book
Format: Paperback
I use it for the machine learning class I teach.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on May 3, 2026

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