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cajon verdura nevera liebherr 9290294Diese Gemseschublade ist ein internes Fach, das fr Khlschrnke von Liebherr entwickelt wurde und es ermglicht, Obst und Gemse lnger frisch zu halten dank der geeigneten Feuchtigkeitskontrolle und Isolierung. Sie ist aus widerstandsfhigen und transparenten Materialien gefertigt, um die Sicht auf den Inhalt zu erleichtern. Es handelt sich um ein Originalersatzteil, das im unteren Bereich des Khlschranks installiert wird und die beschdigte oder defekte
Diese Gemüseschublade ist ein internes Fach, das für Kühlschränke von Liebherr entwickelt wurde und es ermöglicht, Obst und Gemüse länger frisch zu halten dank der geeigneten Feuchtigkeitskontrolle und Isolierung. Sie ist aus widerstandsfähigen und transparenten Materialien gefertigt, um die Sicht auf den Inhalt zu erleichtern. Es handelt sich um ein Originalersatzteil, das im unteren Bereich des Kühlschranks installiert wird und die beschädigte oder defekte Schublade ersetzt.
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Kompatibel mit verschiedenen Modellen von Liebherr-Kühlschränken, die die Schublade mit den Codes 9290294 oder 9290034 verwenden.
| Liebherr KGT 4046 | Liebherr KGT 4002 |
| Liebherr KGT 4040 | Liebherr KG 4060 |
| Liebherr CN 4013 | Liebherr CN 4003 |
| Liebherr CNP 4013 | Liebherr CNP 4003 |
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- Teileart: Gemüseschublade für Kühlschrank
- Kompatible Marke: Liebherr
- Äquivalente Codes: 9290294, 9290034, 21557116
- Maße: Höhe 18 cm, Breite 26 cm, Tiefe 37 cm
- Material: widerstandsfähiger, transparenter Kunststoff
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4.3 ★★★★★
Based on 29 reviews
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Product Reviews
★★★★★ 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
★★★★★ 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
★★★★★ 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.
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Reviewed in the United States on March 1, 2022
★★★★★ 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.
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Reviewed in the United States on February 26, 2022