SKU: 61258381789

marco fornaciari violino

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marco fornaciari violinoFORNACIARI Violin Available in: DSD, Hi Res Audio Track list: GIUSEPPE TARTINI (1692 1770) Sonata No. 13 in B minor Andante Allegro assai Giga: allegro affettuoso (Ed. ZANIBON); 720 FRANZ SCHUBERT (1797 1828) Six Lndlers D374 for solo violin (Ed. HENLE); 240 KAROL JOSEF LIPINSKY (1790 1861) Capriccio Op. 29 No. 3 (Ed. POLSKIE WYDAWNICTWO MUZYCZNE); 250 FRITZ KREISLER (1875 1962) Recitative and Scherzo Caprice Op. 6 (Ed. SCHOTT); 4 BRUNO BETTINELLI

FORNACIARI

Violin


Available in: DSD, Hi-Res Audio

Track list:

GIUSEPPE TARTINI (1692-1770)

  • Sonata No. 13 in B minor

Andante

Allegro assai

Giga: allegro affettuoso

(Ed. ZANIBON); 7‘20“

 

FRANZ SCHUBERT (1797-1828)

  • Six Ländlers D374 for solo violin

(Ed. HENLE); 2’40”

 

KAROL JOSEF LIPINSKY (1790-1861)

  • Capriccio Op. 29 No. 3

(Ed. POLSKIE WYDAWNICTWO MUZYCZNE); 2’50”

 

FRITZ KREISLER (1875-1962)

  • Recitative and Scherzo-Caprice Op. 6

(Ed. SCHOTT); 4’

 

BRUNO BETTINELLI (1913, living)

  • Monologue for solo violin

(Ed. CURCI); 6’50”

 

BÈLA BARTÒK (1881-1945)

Sonata for solo violin

  • Tempo di ciaccona
  • Fuga
  • Melodia
  • Presto

(Ed. BOOSEY and HAWKES); 24’30”

Notes

STEREO 

INSTRUMENT:

Vettori Violin 1981

RECORDING PLACE:

Livorno, San Ranieri Chapel, October 10/14, ‘83

RECORDING:

Studio R.G.C. Livorno

PRODUCTION DIRECTOR:

Giulio Cesare Ricci

SOUND ENGINEER:

Giulio Cesare Ricci

 

MARCO FORNACIARI violin

A few words to justify the program presented in this edition will not be superfluous. At first glance, especially to the European public, it might appear eclectic, if not incoherent, or in any case chosen with outdated concert criteria. Now, without forgetting that editions of this kind are frequent, especially in the USA and USSR, which is certainly not insignificant, it must be said that when it comes to highlighting the qualities and characteristics of a modern instrument like the violin made by Carlo Vettori in 1981, it is natural to lean towards representative music, both in chronological terms and in terms of compositional school. This is how Marco Fornaciari chose to attempt a repertoire that ranges from the early 18th century to the present day.

 

GIUSEPPE TARTINI (1692-1770), from Istria, violinist, sound theorist, and harmony scholar, reveals in his "Sonata No. 13 in B minor" (taken from a group of 24, of which the autograph is preserved in Padua) the stylistically simple breadth, projected towards a barely hinted virtuosity, of the pure and elegant 18th-century expressive lines.

The Six Ländlers for solo violin D 374 by FRANZ SCHUBERT (1797-1828), composed in February 1816, are a small, almost unknown gem by the Viennese master, and present the tones and cadences of the Waltz (which derives from the ländler, a popular rural dance) purified, however, of the symphonic refinements to which the Strausses, among others, have accustomed us.

Scarcely known, KAROL JOZEF LIPINSKI (1790-1861), a Polish violinist, eclectic composer, and orchestra conductor. A rival of Paganini, with whom he did not fare badly in a concert competition, he composed no less than 4 collections of Capriccios, in the Italian style. From Op. 29, "Capriccio" No. 3 is performed, a dazzling cascade of notes in the purest virtuosic style of the early last century.

The "Recitative and Scherzo-Capriccio" Op. 6 by FRITZ KREISLER (1875-1962), on the other hand, is a brilliant example of how the late-Viennese melodic tradition continues in our

century; the great celebrity of this work makes it superfluous to dwell on it.

Of significant interest is the "Monologue for solo violin" by BRUNO BETTINELLI (1913, living), a master who taught more than one generation of musicians from the composition chair of the "G. Verdi" Conservatory in Milan. Not tied to schemes or schools, not influenced by fads, Bettinelli demonstrates how it is possible to compose, today, music enjoyable even by the wider public, provided that the ties with Italian cantabile are not broken.

With the "Sonata for solo violin" dedicated to Y. Menuhin, composed in the classical four-movement structure of the sonata da chiesa, one of the most difficult pieces in all violin literature, both in terms of musical writing and technique, BÉLA BARTOK (1881-1945) contributed decisively to pushing the limits of human possibilities in concert performance.

 

MARCO FORNACIARI (1953), who graduated from the "L. Cherubini" in Florence, obtained his Konzertdiplom in Geneva, under the guidance of Maestro Corrado Romano, receiving the "premier prix avec distinction". He has performed concerts all over the world, both as a soloist and as first violin of the "Solisti Veneti". The instrument used for the performance was built in 1981 by CARLO VETTORI, a Florentine luthier who, in his now thirty years of activity (he began working at a very young age in the workshop of his father Dario, also an esteemed luthier), has achieved numerous successes.


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Par
Carnegie, 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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Richard Hackathorn
Pawtucket, 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.
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Reviewed in the United States on February 26, 2022
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Amazon Customer
Lexington, 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
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Kindle Customer
Boise, 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
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Tommy Jonsson
Alexandria, US
★★★★★ 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

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