SKU: 13465848325

Dynaudio Confidence 60 Speaker (Pair)

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Description

Dynaudio Confidence 60 Speaker (Pair)Your goosebumps will get goosebumps Ultimate performance. Ultimate quality. Ultimate innovation. Meet the new flagship of Dynaudios most advanced speaker range. Sometimes size does matter. The all new Confidence 60, the flagship of the new Confidence family, towers above the outgoing Confidence C4 model. Its unashamedly big; its unselfconscious hi fi royalty. And it sounds like nothing else youve heard. Pull up a chair. While your ears are captivated

Your goosebumps will get goosebumps

Ultimate performance. Ultimate quality. Ultimate innovation. Meet the new flagship of Dynaudio’s most advanced speaker range.

Sometimes size does matter. The all-new Confidence 60, the flagship of the new Confidence family, towers above the outgoing Confidence C4 model.

It’s unashamedly big; it’s unselfconscious hi-fi royalty. And it sounds like nothing else you’ve heard. Pull up a chair.

While your ears are captivated by the Confidence 60’s astonishing power, scale and detail, your eyes will probably be drawn to the single Esotar3 28mm soft-dome tweeter in the middle. Then the twin 15cm MSP midrange drivers with Horizon Surrounds. And, finally to the two 23cm MSP NeoTec Woofers.

The drivers are all new. And they’re all part of the next generation of our innovative DDC (Dynaudio Directivity Control) sound-shaping technology platform. This focuses the sound waves radiating from the speakers into a tight vertical ‘beam’ that avoids reflections from floors and ceilings while maintaining a wide horizontal image. That means a bigger sweet-spot on the couch, a happier audience, and the knowledge that you’re only hearing what the drivers are producing – and not what the room itself is bringing to the party.


The star of the show is the DDC Lens – a machined aluminium waveguide integrated into the precision Compex composite baffle. This ingenious part (the result of hundreds of hours of simulations, prototyping and listening tests) works in conjunction with the baffle shape, the tweeter and the midrange drivers (notice their brand-new Horizon Surround, also part of the system) and the woofers to focus sound waves where they need to go: you.

In fact, everything in the Confidence 60 is designed precisely for that purpose. Even the gasket that decouples the baffle from the cabinet, and the screws that hold everything in place.

The brand-new Esotar3 28mm soft-dome tweeter takes over 40 years of Dynaudio expertise, plus plenty of new learnings from development of the award-winning Esotar Forty unit – and rolls it all into the finest tweeter we’ve ever created. A powerful neodymium magnet system, innovations in airflow routing, the new resonance-busting Hexis inner dome … it all combines to increase detail, clarity and sensitivity.

The new 23cm NeoTec MSP woofers also have neodymium magnets under the hood, and use three layers of glass-fibre in their voice-coil formers for optimum stiffness. The voice-coils themselves are copper (which provides extra moving mass for tighter, more powerful and more controlled bass in this specific driver design). And the entire woofer motor has been designed to harness airflow using an innovative new venting system that’s been machined directly into the magnet.

The Confidence 60’s brand-new MSP midrange drivers are a big departure from previous Dynaudio designs.

They use a radical surround design – the Horizon – that follows the cone’s shape right to the edge of the driver. This reduces the surround’s first resonant mode to effectively increase the whole playing surface area and improve performance. They also sit flush with the baffle to reduce diffractions from the diaphragm and the adjacent tweeter.

Behind the scenes, the basket has been given a new lightweight organic design – one that’s resulted from extensive topology-optimisation simulations. It increases airflow, maintains its stability and rigidity and reduces weight simultaneously without sacrificing performance.

And it’s all finished in our trademark Danish-designed furniture-grade cabinetry. Perfect performance, perfect quality.

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SKU: 13465848325

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4.4 ★★★★★
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Par
Port Orchard, 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.
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Reviewed in the United States on December 20, 2024
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Verified Purchase
Richard Hackathorn
Lake Worth, 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
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Verified Purchase
Amazon Customer
San Leandro, US
★★★★★ 4
Just learning it
Format: Paperback
Nice learning book just have to finish it
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Reviewed in the United States on December 10, 2025
K
Verified Purchase
Kindle Customer
Houston, US
★★★★★ 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
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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