SKU: 68244398661

Night City Skyline Cold Process Soap

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Description

Night City Skyline Cold Process SoapBuy any soap or soap set and get a single packed soap at 50% off. The discount will be applied to the lower priced item. Sets are excluded from 50% discount. Use code: bogo at check out. Packaging: Every soap comes with a foaming net, packed in an elegant gift box. Ingredients: Olive oil, Coconut oil, Palm oil, Distilled water, Lye, Oxide, Mica powder, Fragrance oil Suggested Use: Hand wash, shower, light dish washing Curing: Curing is the process of

Buy any soap or soap set and get a single packed soap at 50% off. The discount will be applied to the lower-priced item. Sets are excluded from 50% discount. Use code: bogo at check out.

Packaging:

Every soap comes with a foaming net, packed in an elegant gift box.

 

Ingredients:

Olive oil, Coconut oil, Palm oil, Distilled water, Lye, Oxide, Mica powder, Fragrance oil

                             

Suggested Use:

Hand wash, shower, light dish-washing

 

Curing:

Curing is the process of allowing saponification to complete and for water to evaporate out. In this way, the soap is dry, harder, milder and the lye non-existent in the finished product. It takes about 4 weeks for a soap to dry and the lye to be totally transformed. All soaps we sell are well cured and ready to use.

 

Sweating:

Decorating accessories, if applicable, are made of melt and pour soap. It has added glycerin that pulls in moisture from the air. This can cause beads of moisture on the bar called sweating or glycerin dew. This is normal and will have no impact on functionality.

 

Miscellaneous:

Please understand that all our products are handmade, and even though artisans make every effort to create only the best, natural blemishes and imperfections are what make each piece unique, captivating and rich in stunning details.

Shipping Notes
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SKU: 68244398661

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4.0 ★★★★★
Based on 25 reviews
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Product Reviews
A
Verified Purchase
Amazon Customer
Los Angeles, 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
Birmingham, 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
T
Verified Purchase
Tommy Jonsson
Alexandria, US
★★★★★ 5
Cover many areas in detail and recommendations for more to read for what's outside
Format: Paperback
Good book!
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on May 4, 2026
M
Verified Purchase
Moses Kayanda
Alexandria, US
★★★★★ 5
One of the best machine learning books...
Format: Paperback, Format: Paperback
Machine Learning can often be intimidating whether you are starting out or already a practitioner. It is easy to get stuck on one concept, walk away frustrated, or just copy that code you find on StackOverflow without really understanding what it does. What the authors of this book, Machine Learning with PyTorch and Scikit-Learn, have managed to do is to keep the reader engaged giving a deeper illustration as to how the concepts work. In this book, you get practical code examples, a detailed explanation of how the various library tools work, and exposure to the mathematical concepts behind machine learning algorithms. In addition, what I like about the book unlike many machine learning books is that the authors have managed to intuitively explain how each algorithm works, how to use them, and the mistake you need to avoid. I have not read a Machine Learning book that better explains Transformers as this one does. The authors have managed to give a detailed dive into this model architecture through well-explained codes and illustrations. As a reader, you walk away having intuitively grasped the concepts of attention and self-attention in ways that will make this crucial NLP architecture clear. You get exposed to pre-trained models from HuggingFace library which really helps to have that hands-on experience working with large datasets. As they have done throughout the book, the authors have broken down those complex mathematical operations into simple explanations that are easy to follow. What I generally like about the book is how it seamlessly connects all the chapters, not throwing off the reader. There are numerous external resources quoted throughout the book. This helps spark that curiosity to dig deeper. In addition, you get introduced to PyTorch, getting exposed to all those sophisticated libraries that help the reader learn how to maximize their compute power. I would say it is not intimidating at all even if you have not used PyTorch before. I would recommend this book to anybody seeking a textbook that is both easy to read and modern in its content. If were to rate the book I will give it a 10/10 as it really applies to both beginners and experienced practitioners, covers all the concepts one needs to apply in their operations, and acts as a quick reference.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on March 1, 2022
G
Verified Purchase
Gabe Rigall
Alexandria, US
★★★★★ 5
Thorough Primer for Machine Learning and PyTorch
Format: Paperback
BLUF: A thorough primer for machine learning enthusiasts with plenty of theory to underscore its many practical examples. A definite must-have for anyone looking to add PyTorch to their machine learning tool belt. PROS: - Extremely thorough (if not comprehensive). I really appreciate that this book doesn't just thrust one into building models with PyTorch. It starts at the "beginning" and provides examples, theory, additional resources, and citations along the way. - Theory. Those whose calculus and linear algebra courses ended many years ago will appreciate (if not remember exactly) the mathematical theory and notation that accompanies almost every paragraph. This book gives one the opportunity to "dig deeper" or stay in the shallows until the notation stops. - Python. Rather than simply utilizing Scikit-Learn to illustrate concepts and introduce models, this book contains many sections where models (such as a Perceptron) are coded from the ground up so the reader can fully understand the underlying mechanics. Python enthusiasts will nerd out. Parents of small children might want to skip a few pages. - Graphs, charts, and graphics. There are plenty of places where a drier text might have foregone the use of graphs. This text does not. It does however refrain from overusing them. - PyTorch. This should be obvious from the title, but this text prioritizes PyTorch instead of TensorFlow. This is especially helpful for those looking for an alternative to Keras and TensorFlow as the PyTorch API is very user-friendly. CONS: - Almost too much code. This isn't a true "con" but anyone wanting to emulate or follow along with the examples would do well to get the digital edition so they can copy and paste. - Length and complexity. Anyone hoping for a "quick read" or a "quick start guide" will be disappointed. This book hovers somewhere between an undergraduate primer and a graduate-level text for length and readability. This is not to say that it's difficult to read, merely that there are other "quick start" / "practical" texts out there that cater more to a lay audience.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on February 26, 2022

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