SKU: 89405823370

Signac, 1863-1935

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Signac, 1863-1935This book, the catalogue of the first retrospective of the work of the French Neoimpressionist artist Paul Signac to be held in nearly forty years, accompanies the 2001 exhibition organised by the Reunion des Musees Nationaux Musee d'Orsay, Paris, the Van Gogh Museum, Amsterdam, and The Metropolitan Museum of Art, New York. This long overdue tribute to Signac's power of expression and artistic influence features some two hundred paintings, drawings,

This book, the catalogue of the first retrospective of the work of the French Neoimpressionist artist Paul Signac to be held in nearly forty years, accompanies the 2001 exhibition organised by the Reunion des Musees Nationaux/Musee d'Orsay, Paris, the Van Gogh Museum, Amsterdam, and The Metropolitan Museum of Art, New York. This long overdue tribute to Signac's power of expression and artistic influence features some two hundred paintings, drawings, watercolours, and prints from public and private collections worldwide. Fully illustrated in colour and discussed in individual entries, these works offer an unprecedented overview of Signac's fifty-year career. Signac's artistic development began with the luminous plein air paintings he made in the early 1880s which reveal the lessons he absorbed from Monet, Guillaumin, and other leading Impressionists. From 1884 until 1891 Signac's close association with Georges Seurat encouraged his explorations of colour harmony, contrasts, and Neoimpressionist technique. In the scintillating works of his maturity the rigours of Pointillism gave way to richly patterned, decorative colour surfaces. In a series of essays the exhibition's curators discuss Signac's richly interesting career from a variety of perspectives. John Leighton, Director of the Van Gogh Museum, provides an introductory essay that chronicles Signac's triumphs as a painter. The well-known Signac scholar Marina Ferretti Bocquillon focuses on Signac's achievements as a draftsman and watercolourist, and Sjraar van Heugten, Chief Curator of the Van Gogh Museum, summarises Signac's activity as a printmaker. Anne Distel, Chief Curator of the Musee d'Orsay, examines Signac's role as a promoter of his own works and those of his colleagues and describes a host of other activities - beyond painting - that engaged Signac's interest. The final essays in this volume shed new light on Signac's appreciation of the works of his predecessors, contemporaries, and followers - as evidenced in his artworks, in his published and unpublished writings, and in his private collection. Susan Alyson Stein, Associate Curator of European Paintings, The Metropolitan Museum of Art, examines the ways Signac understood the genius of such painters as Delacroix, Monet, Renoir, Cezanne, Van Gogh, Bonnard, and Matisse. Marina Ferretti Bocquillon explores the Signac's role as a collector, providing a wealth of new information about the works he owned by fellow artists. Contributor Kathryn Calley Galitz is Research Associate in the Department of European Paintings at The Metropolitan Museum of Art, New York. Lavishly illustrated with comparative and documentary photographs, the volume includes an annotated chronology and a map that pinpoints the sites depicted in Signac's works.

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SKU: 89405823370

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Amazon Customer
Phoenix, 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
Omaha, 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
New York, 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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Moses Kayanda
Lowell, US
★★★★★ 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
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Gabe Rigall
Dallas, US
★★★★★ 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

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