SKU: 82918294451
coconut palm sugar vs sugar

coconut palm sugar vs sugar Coconut Palm Sugar - NY Spice Shop

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Description

coconut palm sugar vs sugar Coconut Palm Sugar - NY Spice ShopToasty, rich aroma Finely granulated texture Also known simply as coconut sugar Subtle sweetness with notes of caramel Coconut palm sugar (also known as coconut sugar) is a natural sweetener made from the sap of the coconut palm flower. Like maple sugar, the coconut palms sap is harvested and heated until it dries into sugar crystals. This minimally processed sugar has a less intense sweetness than heavily processed white sugar, with light, toasty

 

  • Toasty, rich aroma
  • Finely granulated texture
  • Also known simply as coconut sugar
  • Subtle sweetness with notes of caramel

 Coconut palm sugar (also known as coconut sugar) is a natural sweetener made from the sap of the coconut palm flower. Like maple sugar, the coconut palm’s sap is harvested and heated until it dries into sugar crystals. This minimally processed sugar has a less intense sweetness than heavily processed white sugar, with light, toasty notes of caramel. This makes it an excellent substitute for traditional brown sugar, and it can also replace other sugars in most recipes.

Coconut palm trees grow throughout Southeast Asia, principally in the Philippines and Indonesia. A coconut palm tree can produce sap for 20 years and can grow well in arid climates with minimal water. Coconut palms can produce more sugar per acre than sugar cane. Often, when a coconut palm tree has stopped producing coconuts (generally around the age of 50+ years old), it is "retired" to sap collection to produce coconut palm sugar

Common Uses: 

  • Natural sweetener in tea, coffee, and hot drinks
  • Used in baking and desserts
  • Sweetening cereals, granola, oatmeal
  • Added to sauces, marinades, and dressings
  • Stirred into smoothies or yogurt

How to Use?

  • Use 1:1 in place of white or brown sugar in recipes
  • Sweeten tea, coffee, smoothies
  • Add to oatmeal, yogurt, or sauces
  • Excellent in baking and dessert recipes

Best Recipe Pairings: 

  • Coffee & cappuccino
  • Sweet breads and muffins
  • Caramel desserts
  • Pancakes and waffles
  • Nut butters and fruit sauces

Serving Size: Typical serving size: 1 teaspoon (4–5 g) — provides sweetness and a rich caramel note.

Shelf Life: 12–24 months when stored in sealed packaging in a cool, dry place. Best kept airtight to prevent moisture absorption.

Botanical/Scientific Name: Cocos nucifera (sap harvested from coconut palm flowers)

Origin: Typically harvested in Indonesia and similar tropical regions, then processed and packaged.

Storage Directions: Store in a cool, dry place; airtight container after opening to prevent clumping and moisture.

Ingredients: Coconut Sap Sugar

Allergen Information: Shares equipment with peanuts, tree nuts, wheat, milk, soy, eggs, & sesame. 

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SKU: 82918294451

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Zygerian99
Birmingham, 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.
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Reviewed in the United States on January 21, 2020
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Shannon
West Palm Beach, 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!!
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Reviewed in the United States on November 30, 2025
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William P Ross
Boise, 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.
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Reviewed in the United States on March 15, 2017
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Adam
Battle Creek, 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.
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Reviewed in the United States on May 22, 2026
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Amazon Customer
Massapequa, US
★★★★★ 5
Comprehensive! The Bible of Deep Learning!
This book has by far surpassed my expectations! I have purchased many machine learning and deep neural network books in the past, but nothing has ever come close to this book! First of all, it is written by the fathers of Deep Learning, and is therefore an authority. Secondly, the book is broken into three parts: 1. A math overview and refresher. 2. Deep Learning applications and 3. Research in Deep Learning. I can't help but go through this book from front to back. It is a smooth read, and every sentence written is meaningful. These guys know their stuff! And after you read this book, YOU WILL ALSO know your stuff! If you feel daunted by the price, just remember, you get what you pay for! I'd say they could easily charge about $300+ for this book, but they are doing everyone a very kind favor by ONLY charging this reasonable amount. You get A LOT of bang for your buck with this purchase. I hesitated at first about buying this book because of the price, but I am soooooo happy that I did! Worth every penny! Look no further, get this book and start your Deep Learning journey!!
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Reviewed in the United States on July 14, 2017

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