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Burmese Curly Super Drawn Bundles – Vietnamese Hair Extensions Natural BlackBurmese Curly Super Drawn Bundles Vietnamese Hair Extensions Natural Black To register as a wholesale customer directly with the factory, please contact us through the following support channels: WhatsApp: https: wa. me 84386228622 https: wa. me 84966206679 https: wa. me 849612325513 https: wa. me 84866942868 https: wa. me 84352826123 2. Instagram: https: www. instagram. com venahairfactory Unique Selling Points (USPs) of Vena Hair Products 100%
Burmese Curly Super Drawn Bundles – Vietnamese Hair Extensions Natural Black To register as a wholesale customer directly with the factory, please contact us through the following support channels:
2. Instagram:
https://www.instagram.com/venahairfactory
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Unique Selling Points (USPs) of Vena Hair Products
• 100% VIETNAMESE HAIR:
Vena Hair supplies only the finest — 100% Raw Vietnamese Hair. The hair is selected from young women in the highland areas of Northwest Vietnam, where the cool year-round climate and limited sun exposure produce thick, strong strands. This lets you do anything you want with your hair; healthy hair is easier to maintain, and this raw material provides hair longevity of 6–8 years.
•. EXCEPTIONAL THICKNESS – SUPER DRAWN QUALITY:
Our hair is distinguished by its exceptional thickness, reaching the Super Drawn standard. It is significantly thicker than the offerings of any other supplier in Vietnam, ensuring a voluminous and luxurious look that stands out in the market.
• DOUBLE MACHINE WEFT:
You no longer have to worry about shedding when washing or brushing your bundles. Starting this month, we’ve upgraded to Double Machine Weft technology with reinforced double-stitched tracks. In addition, we’ve increased the hair density sewn into each track to a super-full level — giving you thicker, stronger, and longer-lasting bundles.
• VERSATILE AND DURABLE:
The strength and health of our hair make it ideal for styling. You can curl, dye, or transform it into any style you desire, all while maintaining its premium quality and durability.
• CHECK ROOT:
Exclusively at Vena Hair, we perform a 100% manual Check Root process. Right after each bundle is sewn, our craftsmen carefully inspect and remove even the few strands that may run in the opposite direction. This extra step guarantees your hair stays smooth and free from tangling or frizzing during brushing or washing.
When you choose Vena Hair, you are guaranteed top-quality hair made from 100% authentic Vietnamese human hair of the highest standard, free from any blending.
We manufacture and supply 100% Vietnam human hair .
- Hair Grade : Vietnamese Hair
- Human Hair Type : Vietnamese Hair
- Weight : 100 Grams/Pcs
- Style : Kinky Curl, Silky Straight Wave, telephone curl, Jerry Curl, Water Wave, Deep Wave, Other, Italian curl, Kinky Straight, Regular Wave, Afro Wave, Natural Wave, Italian Wave, Big Curl, Loose Deep Wave, French Curl, Body Wave, Loose Wave, FUMI, Super Wave, Spring Curl, Yaki, Straight
- Suitable Dying Colors : ALL COLORS
- Hair Weft Machine : Double Machine Weft
- Chemical Processing : None
- Longest Hair Ratio : >=80%
- Length : 8-40inch
- Hair color : Customized Color
- Texture : Customized Texture
- MOQ : 1 Piece
- Hair type : 100% Human Hair Weft
- Hair End : Thick Healthy Ends
- Payment : Visa.TT.Western Union.Money Gram.Paypal
- Shipping : DHL\UPS\FEDEX
- Advantage : Whoslesale
⏳ Production & Delivery Policy – Vena Hair
At Vena Hair, every order is specially produced on demand using 100% freshly donated raw hair in its best natural state. We never use stockpiled hair to ensure your products arrive at the highest possible quality.
Estimated Production Time
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Bundles (Straight): 8–10 days
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Bundles (Curly): 10–12 days
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Closure/Frontal: 8–10 days
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Wigs: 8–10 days
Actual production may be slightly faster or slower (± 1–2 days).
Shipping
We partner with UPS/DHL – the fastest global couriers, ensuring your order reaches you in just 2–4 business days after production.
Your order is crafted with care, delivered with speed, and guaranteed with quality.
Shipping Notes
- Free Standard Shipping on $100+ Orders to the USA.
- Except Preorder products are shipped in 48 hours.
- Delivery to the USA:
- Standard Shipping : 3-10 business days
- If time is of the essence, please consider selecting expedited delivery for faster service.
Exchange/Return Notes
- We offer a 30-day return/exchange service after receiving.
- Final sale items are not eligible for returns or exchanges.
- To process your return/exchange, please contact us at [email protected]
- Please click here for more details>>> Return & Exchange Policy
4.0 ★★★★★
Based on 5 reviews
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Product Reviews
★★★★★ 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
★★★★★ 5
Cover many areas in detail and recommendations for more to read for what's outside
Format: Paperback
Good book!
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Reviewed in the United States on May 4, 2026