SKU: 81839500566

vidaXL Aufbewahrungsbett mit Matratze Taupe 90 x 190 cm Holzwerkstoff

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Description

vidaXL Aufbewahrungsbett mit Matratze Taupe 90 x 190 cm HolzwerkstoffDieses moderne Stauraumbett mit Matratze ist eine stylische, aber praktische Ergnzung fr jedes Schlafzimmer. Mit klaren Linien integriert es einen erstklassigen Lattenrost und eine Matratze, die auf deine Komfortbedrfnisse abgestimmt ist. Es wurde fr moderne Innenrume gestaltet, sodass dieses Bett nicht nur gut aussieht, sondern auch hochwertigen Stauraum bietet perfekt fr alle, die Effizienz ohne Kompromisse beim Design suchen. Dank des hydraulischen

Dieses moderne Stauraumbett mit Matratze ist eine stylische, aber praktische Ergänzung für jedes Schlafzimmer. Mit klaren Linien integriert es einen erstklassigen Lattenrost und eine Matratze, die auf deine Komfortbedürfnisse abgestimmt ist. Es wurde für moderne Innenräume gestaltet, sodass dieses Bett nicht nur gut aussieht, sondern auch hochwertigen Stauraum bietet – perfekt für alle, die Effizienz ohne Kompromisse beim Design suchen. Dank des hydraulischen Aufzugsmechanismus hast du viel Platz unter dem Bett, was deine Wohnfläche optimiert und für Ordnung sorgt. Rechteckige Form: Das Stauraumbett hat eine rechteckige Form, die perfekt für die meisten Schlafzimmer ist. Sein modernes Design sieht gut aus und passt sich jeder Raumgestaltung an. Ob an der Wand oder frei im Raum, es zieht die Blicke auf sich und verbessert die Ästhetik deines Zimmers. Hochwertige Materialien: Hergestellt aus Spanplatte, bietet der Rahmen stabilen Halt, während das Polyester-Headboard etwas Luxus bringt. Der Metallrahmen sorgt für Langlebigkeit und die mittelharte Matratze garantiert maximalen Komfort. Die cleveren Materialien verhindern ein Durchhängen und formen sich optimal an deinen Körper, sodass sie lange halten. Hydraulischer Liftmechanismus: Der Mechanismus funktioniert sanft und kontrolliert, damit du einfach auf den Stauraum zugreifen kannst. Mit einem einfachen Handgriff hebst du das Bett hoch und kommst einfach an den großen Platz darunter. Dieses Design ist super für Leute, die mehr Stauraum brauchen und macht die Ordnung von saisonalen Bettwaren oder Klamotten einfacher. Polyester-Headboard und Matratze: Die Kopf- und Matratzenpolster bestehen aus Polyester, einem Material, das nicht einläuft oder sich dehnt. Die mittlere Festigkeit der Matratze passt für alle Schlafpositionen und balanciert Weichheit mit der nötigen Unterstützung für entspannende Nächte, egal ob du auf dem Rücken, der Seite oder dem Bauch schläfst. Einfache Montage und Pflege: Der Aufbau ist kinderleicht und braucht fast kein Werkzeug – ein Schraubendreher reicht, und das geht am besten zu zweit. Jedes Teil ist so gestaltet, dass es einfach zu handhaben ist, und du kannst dich darauf konzentrieren, deine neuen Möbel zu genießen, ohne dich mit kompliziertem Aufbau herumzuschlagen. Die Pflege ist einfach: regelmäßiges Abwischen genügt.

  • Farbe: Taupe
  • Rahmenmaterial: Metall
  • Kopfteilmaterial: Polyester
  • Matratzenhärte: Mittel
  • Gesamtabmessungen: 194 x 90 x 88 cm (L x B x H)
  • Matratzengröße: 90 x 190 cm
  • Maximale Personenzahl: 1
  • Max. Gewicht: 140 kg
  • Betthöhe:48,5 cm
  • Geräumiger Stauraum unter dem Bett
  • Hydraulischer SeitLiftmechanismus
  • Nur für den Innenbereich
  • Montage erforderlich: Ja
  • Lieferumfang:
  • 1 x Bett (90 x 194 cm)
  • 1 x Matratze (90 x 190 x 20 cm)
  • 1 x Matratzentopper (90 x 190 x 5 cm)
  • 1 x Kopfteil
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SKU: 81839500566

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4.6 ★★★★★
Based on 26 reviews
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Par
Boise, US
★★★★★ 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.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on December 20, 2024
R
Verified Purchase
Richard Hackathorn
Omaha, US
★★★★★ 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.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on February 26, 2022
A
Verified Purchase
Amazon Customer
Alexandria, 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
Louisville, 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
San Leandro, 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

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