SKU: 68919650288

sot tartar chungwoo 1 7kg

$96750.00 $107500.00
Save 10%
No.2 Bestseller on imagineryskincare.co.za >

Shipping Estimate
USA
  • USA
  • CAN

Ships within 48 hours · Estimated delivery Jul 10 - Jul 15

Promo Codes Available:

For Your Every Summer RSVP, with Code: SUMMER15

Description

sot tartar chungwoo 1 7kgKhng phi ketchup hay tng t, st tartar mi chnh l la chn chn i khi n cng cc mn hi sn chin nh tm, c. y lun l nhng b i kt hp cng nhau v c phc v ph bin ti nhiu nh hng, qun n. St tartar l mt loi st chm a nng c mu trng kem dng st, thnh phn chnh c lm t mayonnaise, da chua thi nh (da chut mui), chanh, gim cng mt s loi gia v khc. Tt c c phi trn v ha quyn vo nhau to nn mn st tartar chua ngt, bo thm rt cn i, hng v hp dn nhng li khng gy ngn. St tartar thng c dng

Không phải ketchup hay tương ớt, sốt tartar mới chính là lựa chọn chân ái khi ăn cùng các món hải sản chiên như tôm, cá. Đây luôn là những bộ đôi kết hợp cùng nhau và được phục vụ phổ biến tại nhiều nhà hàng, quán ăn.

Sốt tartar là một loại sốt chấm đa năng có màu trắng kem dạng sệt, thành phần chính được làm từ mayonnaise, dưa chua thái nhỏ (dưa chuột muối), chanh, giấm cùng một số loại gia vị khác. Tất cả được phối trộn và hòa quyện vào nhau tạo nên món sốt tartar chua ngọt, béo thơm rất cân đối, hương vị hấp dẫn nhưng lại không gây ngán.

Sốt tartar thường được dùng để rưới lên hoặc chấm kèm với các món hải sản chiên như cá và khoai tây chiên, tôm chiên, bánh cua (crab cakes), bánh cá hồi,…Khi dùng sốt tartare phết lên từng miếng hải sản chiên giòn nóng hổi sẽ giúp làm mềm độ giòn và giữ ẩm, giảm bớt tình trạng khô miệng khi ăn, hơn nữa vị chua của giấm và chanh có trong sốt sẽ giúp cân bằng vị đậm đà vị tanh của hải sản, khiến cho bữa ăn trở nên ngon miệng hơn.

Ngoài đồ chiên, sốt tartar còn được sử dụng linh hoạt và sáng tạo trong nhiều món ăn khác như chấm cùng súp lơ chiên, rau củ nướng, khoai tây nướng, trứng luộc, salad, bánh mì pita, sandwich kẹp thịt,…

Sốt tartar Chungwoo là sản phẩm nhập khẩu trực tiếp từ Hàn Quốc, được làm từ nguồn nguyên liệu chất lượng dựa trên công thức đặc biệt, đảm bảo cung cấp đến người dùng một chai sốt tartar hoàn hảo, chuẩn hương vị.

Sản phẩm có quy cách chai 1.7kg siêu nhiêu và tiết kiệm, thích hợp dành cho quý đối tác đang kinh doanh ẩm thực, hứa hẹn sẽ làm cho các món ăn của quán đã ngon lại còn cuốn.

Thành phần: Mayonnaise [dầu thực vật (nước ngoài: Mỹ, Brazil, Paraguay,...), nước tinh khiết, dung dịch lòng đỏ trứng [lòng đỏ trứng (Trung Quốc), muối tinh luyện (Trung Quốc), giấm lên men, đường, muối tinh luyện (nội địa)], nước tinh khiết, đường, dưa chuột muối cắt lát [dưa chuột muối 60% (Ấn Độ: dưa chuột, muối, canxi clorua), gia vị [fructose khác, giấm, dung dịch nước muối, kali penmanganat (chất bảo quản)], hành (nội địa), giấm lên men, tinh bột biến tính, muối tinh luyện, vitamin C, mùi tây (parsley flake), hương chanh, calcium disodium EDTA (phụ gia)

Khối lượng tịnh: 1.7kg/chai

Bảo quản: Nơi khô ráo, thoáng mát và tránh ánh nắng mặt trời. Bảo quản lạnh sau khi mở nắp

Hướng dẫn sử dụng: Sử dụng cho các món ăn. Dùng để chấm với các món hải sản chiên hoặc chế biến với món ăn tùy thích

Hạn sử dụng: 12 tháng kể từ ngày sản xuất

Xuất xứ: Hàn Quốc

Shipping Notes
  • Free Standard Shipping on $100+ Orders to the USA.
  • Except Preorder products are shipped in 48 hours.
  • Delivery to the USA:
  1. 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
SKU: 68919650288

Discover Niche Categories That Outsell

Top-Converting Item to Boost Your Average Order

4.5 ★★★★★
Based on 1411 reviews
Sort
Highest Rating
Newest First
Oldest First
Product Reviews
X
Verified Purchase
0x00000000:00000000
Draper, US
★★★★★ 5
Excellent book, possibly currently unique in coverage of latest ideas
This book is possibly currently unique in its coverage of the latest ideas in the field of deep learning -- and it is a very convenient and good survey of fundamental concepts (linear algebra, optimization, performance metrics, activation function types), different network types (multi-layer perceptron, convolutional neural networks, and recurrent neural networks), practical considerations (data set, training and validation, implementation), and applications (comments on existing real-world/commercial uses). The final 235 pages of the content portion of the book is dedicated to topics in "Deep Learning Research", and these topics are truly at the current frontier. Another reviewer said that one could gain the same knowledge of cutting-edge research by reading all of the latest papers (from academia and industry), but the "research" section of this book offers the following: Selection of the most notable research by the very experienced authors of the book, and collection of similar research in to a broader discussion of themes, and the additional insights. The book covers very advanced and new ideas currently being explored, and it is very nice to be able to have a consistent and coherent presentation of all of those ideas. However, the book is also packed with valuable observations and pointers about more basic aspects of deep learning implementations and practices -- and such commentary is in depth and includes substantial analysis and mathematical derivation (in an intuitive presentation that often includes graphs illustrating the phenomenon). As someone with an intermediate level of knowledge and experience of neural networks, I am really grateful for this book, because seems like the ideal resource for learning cutting-edge ideas and practices, with context. The book has excellent scope and depth, and I am confident that anyone with a solid background in linear algebra, calculus, statistics, and general machine learning, and basic neural networks (multi-layer perceptrons) will find this book to be very exciting and perhaps unique in its ability to take the reader to the next level and a new frontier. I was personally excited to learn about the idea of representing the dependencies of intermediate quantities by directed graphs, and how this can be used to perform calculations for recurrent neural networks efficiently. And I think the long chapter on recurrent neural networks is very helpful. Having said all of this, I think only people with significant working knowledge and experience with neural networks and mathematics -- people whose academic or professional focus has been neural networks for at least a year or two -- would benefit from this book. This book answers a lot of the deeper questions that one is likely to have while developing a solid understanding of the fundamentals, and that's one of the book's tremendous values, but this book assumes an understanding of the fundamentals (but does briskly cover the basics). I think this book is a perfect follow-up book for the excellent book "Neural Network Design (2nd edition)" by Hagan, Demuth, Beale, and de Jesus, and I highly recommend the latter for gaining the solid background needed to have a thrilling experience with the "Deep Learning" book. In summary, I am very glad this "Deep Learning" book was written, and I think the "Deep Learning" book will be a great benefit to a lot of people, and to the evolution of the field.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on April 18, 2017
Z
Verified Purchase
Zygerian99
Omaha, US
★★★★★ 5
The definitive guide to becoming a researcher in the field
Format: Hardcover
This is not a coding book. I see a lot of negative reviews around the expectation that this book would teach the reader how to quickly build machine learning systems and write code. This book is not for that audience. If you just want to build applications, don't worry about how deep learning works. It's akin to needing to understand how an engine works just to drive a car. If you are looking for a coding resource, try: https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1492032646/ref=sr_1_4?keywords=machine+learning+tensorflow&qid=1579608765&sr=8-4 . And even with that book, the material still goes far beyond what you need - use it as a light reference. I bought this book as an aspiring machine learning researcher, and towards that end, it is the best resource available in print (still true as of 2020). For instance: The first 5 chapters are timeless. These are things that were mostly established 20 or 30 years ago and beyond and are mostly STEM fundamentals at this point. There are whole textbooks dedicated to each of those chapters, but the authors provide a quick refresher and overview of probably 80% of what you'll encounter in deep learning. If you haven't previously learned each of these subtopics, you'll probably want to study them individually since they are the key to innovating (linear algebra, probability & stats, numerical computation, machine learning fundamentals). Chapters 6 thru 9 are the foundation of deep learning. We're about 12 years into seeing rapid change in the deep learning space, yet all of these principles and techniques still hold (many recent innovations are still relying on Convolutional models in 2020, which is the most layered/complex topics in those chapters). Therefore, I'd wager that these chapters are also fairly stable knowledge that is worth internalizing if you want to be deeply involved in the future of machine learning. Chapters after 9 are mostly experimental topics, and many of them are already the wrong strategies for optimal results. But there are interesting ideas in here that you'll often encounter in the wild, so it's good exposure to various topics. But probably not worth much of your time. And lastly, there is good history in here from people who know the space intimately. It's a good way to piece together the developments and learn the lexicon of deep learning so you can have intelligent conversation with experts.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on January 21, 2020
S
Verified Purchase
Shannon
Lexington, US
★★★★★ 5
The best DL/ML book I have ever seen!!
Format: Hardcover
Fantastic deep-learning book! The logic is very easy to follow, but the content is very thorough when it comes to explaining the theories behind it, making it perfect for beginners as well as math and CS students. The best DL/ML book I have ever seen!!
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on November 30, 2025
W
Verified Purchase
William P Ross
Bozeman, US
★★★★★ 5
Comprehensive Look At An Incredibly Complex Topic
Format: Hardcover
Deep Learning is an advanced book with great explanations and details. There is a heavy math focus with the book's beginning chapters detailing the necessary linear algebra and probability that one will need to understand deep learning. I liked that the author's chose to cover only the parts of these subjects which are relevant to deep learning. There are many interesting philosophical sections in the book as well. Just about when I was feeling overwhelmed with the complexity of the mathematics the authors take a step back and cover the foundations of deep learning such as borrowing concepts from human learning. There was an interesting dicussion about the early studies done on the vision of cat's and monkey's in the 1970s. The text covers the entire history of deep learning and the bibliography is hundreds of sources. It is clear this is the most comprehensive text available about deep learning. For anybody interested in this topic this book is a mandatory read. There are sections about machine learning as well, which makes sense because deep learning is a subset of machine learning. These sections focused on the machine learning concepts which are most relevant to deep learning. The book was well organized and divided into three parts which cover mathematics related to deep learning, typical deep learning techniques, and then more experiment learning techniques. Often the author's state when a technique works well or when it does not, and which types of data works best for the technique. Just a warning, the math in this book is highly complex. It requires a lot of work to go through this book, but the effort will be well rewarded.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on March 15, 2017
A
Verified Purchase
Adam
Dallas, US
★★★★★ 4
Too Dry.
Format: Hardcover
This was a required textbook for my class in college. I think it was too dry. The book titled Deep Learning: From Curiosity To Mastery is much more approachable.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on May 22, 2026

recommand products

UNISEX T-Shirt: Dragon Moon
35.00
Keychains (Schlüsselanhänger)
10.00
Baldurs Gate 3: White Shadowheart (Digital Set)
60.00
Cosplay Clearance: Crystal Rose Zyra
60.00
Genshin Impact: Yoimiya (Signed A4 Print)
15.00