SKU: 10531201961

AVC-X2850H - Outlet

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Description

AVC-X2850H - OutletOutlet: Webshop retour, geopende verpakking. Product is zo goed als nieuw. Levering met originele doos, accessoires en garantie. Denon AVC X2850H 7. 2 kanaals 150W 8K AV versterker met HEOS Met 150 watt per kanaal levert de 7. 2 kanaals Denon AVC X2850H krachtig geluid, 8K video en een meeslepende Dolby Atmos ervaring. Dankzij HEOS stream je draadloos je favoriete muziek, terwijl de intutieve installatie en nauwkeurige ruimtekalibratie zorgen voor

Outlet: Webshop retour, geopende verpakking. Product is zo goed als nieuw. Levering met originele doos, accessoires en garantie.

Denon AVC-X2850H

7.2-kanaals 150W 8K AV-versterker met HEOS®

Met 150 watt per kanaal levert de 7.2-kanaals Denon AVC-X2850H krachtig geluid, 8K-video en een meeslepende Dolby Atmos®-ervaring. Dankzij HEOS® stream je draadloos je favoriete muziek, terwijl de intuïtieve installatie en nauwkeurige ruimtekalibratie zorgen voor optimaal geluid, of je nu gamet, streamt of films kijkt.

  • 7.2 kanalen
  • 8K Ultra HD
  • Powered by HEOS™
  • 150 watt per kanaal
  • 3D-audio
  • 6 HDMI-ingangen

De volgende grote stap voor je thuisbioscoop

Of je nu een surround sound-systeem opzet of je hele huis van audio voorziet, de AVC-X2850H biedt de kracht en flexibiliteit voor een moeiteloze installatie en onvergetelijke ervaringen met 150 watt versterking per kanaal, 7.2-kanaals flexibiliteit, ondersteuning voor Dolby Atmos® en DTS:X® en ultra-gedetailleerde 8K-video passthrough.

Levensecht geluid

De AVC-X2850H levert puur, high-fidelity geluid met verbluffende helderheid en diepte, waarbij elk detail behouden blijft zoals de makers het bedoeld hebben. Van de kleinste fluistering tot de grootste explosie, je hoort elk moment met ongekende precisie.

Jouw entertainment, jouw manier

De AVC-X2850H levert krachtig en dynamisch geluid via 7.2 kanalen met 150 watt per kanaal, ongeacht de configuratie. Met zes HDMI-ingangen en twee uitgangen, waarvan drie geschikt zijn voor 8K, sluit je al je apparaten moeiteloos aan.

Waarom kiezen voor een Denon AVR?

Al meer dan een eeuw staat Denon aan de top van audiotechnologie – van de eerste cd-speler tot de eerste Dolby Atmos- en 8K-AV-receivers. We voldoen niet alleen aan de standaard, we bepalen hem. Met Denon ervaar je krachtig, meeslepend geluid dat meegroeit met je thuisbioscoop.

HEOS multiroom audio

Verbind Powered by HEOS-luidsprekers, sound bars, hi-fi-systemen en receivers via wifi voor eenvoudig draadloos geluid in het hele huis. Stream muziek in hoge resolutie van toonaangevende diensten, deel geluid van aangesloten apparaten en bedien alles via de app of met je stem. Speel in elke kamer iets anders af of groepeer kamers voor één naadloze luisterervaring.

Beter gamen met beter geluid

De AVC-X2850H is geoptimaliseerd voor next-gen consoles, met 4K/120Hz doorgifte, Variable Refresh Rate (VRR), Auto Low Latency Mode (ALLM) en Quick Frame Transport (QFT) voor ultra-reactieve gameplay. Hoor elk detail met Dolby Atmos en DTS:X 3D-geluid dat meebeweegt met de actie zodat je sneller reageert, dieper opgaat in de game en gefocust blijft, of je nu open werelden verkent of online strijdt.

Channel Level Monitoring

Channel Level Monitoring toont realtime het geluidsniveau van je speakers op je tv, ideaal om je systeem te begrijpen of te laten zien.

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SKU: 10531201961

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4.8 ★★★★★
Based on 26 reviews
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Product Reviews
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Verified Purchase
Par
Battle Creek, 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
Phoenix, 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
New York, 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
New York, 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
Draper, 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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