mathematical pictures data science di foucart simon (36 risultati)

Paperback. Condizione: Fine.

Condizione: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.

Condizione: Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service.

Condizione: New. New edition NO-PA16APR2015-KAP.

Condizione: New.

Condizione: New.

Paperback or Softback. Condizione: New. Mathematical Pictures at a Data Science Exhibition 1. Book.

Condizione: New.

Condizione: New. In.

PF. Condizione: New.

Taschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - This text provides deep and comprehensive coverage of the mathematical background for data science, including machine learning, optimal recovery, compressed sensing, optimization, and neural networks. In the past few decades, heuristic methods adopted by big tech companies have complemented existing scientific disciplines to form the new field of Data Science. This text embarks the readers on an engaging itinerary through the theory supporting the field. Altogether, twenty-seven lecture-length chapters with exercises provide all the details necessary for a solid understanding of key topics in data science. While the book covers standard material on machine learning and optimization, it also includes distinctive presentations of topics such as reproducing kernel Hilbert spaces, spectral clustering, optimal recovery, compressed sensing, group testing, and applications of semidefinite programming. Students and data scientists with less mathematical background will appreciate the appendices that provide more background on some of the more abstract concepts.

Paperback. Condizione: new. Paperback. This text provides deep and comprehensive coverage of the mathematical background for data science, including machine learning, optimal recovery, compressed sensing, optimization, and neural networks. In the past few decades, heuristic methods adopted by big tech companies have complemented existing scientific disciplines to form the new field of Data Science. This text embarks the readers on an engaging itinerary through the theory supporting the field. Altogether, twenty-seven lecture-length chapters with exercises provide all the details necessary for a solid understanding of key topics in data science. While the book covers standard material on machine learning and optimization, it also includes distinctive presentations of topics such as reproducing kernel Hilbert spaces, spectral clustering, optimal recovery, compressed sensing, group testing, and applications of semidefinite programming. Students and data scientists with less mathematical background will appreciate the appendices that provide more background on some of the more abstract concepts. This text explores a diverse set of data science topics through a mathematical lens, helping mathematicians become acquainted with data science in general, and machine learning, optimal recovery, compressive sensing, optimization, and neural networks in particular. It will also be valuable to data scientists seeking mathematical sophistication. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Paperback. Condizione: new. Paperback. This text provides deep and comprehensive coverage of the mathematical background for data science, including machine learning, optimal recovery, compressed sensing, optimization, and neural networks. In the past few decades, heuristic methods adopted by big tech companies have complemented existing scientific disciplines to form the new field of Data Science. This text embarks the readers on an engaging itinerary through the theory supporting the field. Altogether, twenty-seven lecture-length chapters with exercises provide all the details necessary for a solid understanding of key topics in data science. While the book covers standard material on machine learning and optimization, it also includes distinctive presentations of topics such as reproducing kernel Hilbert spaces, spectral clustering, optimal recovery, compressed sensing, group testing, and applications of semidefinite programming. Students and data scientists with less mathematical background will appreciate the appendices that provide more background on some of the more abstract concepts. This text explores a diverse set of data science topics through a mathematical lens, helping mathematicians become acquainted with data science in general, and machine learning, optimal recovery, compressive sensing, optimization, and neural networks in particular. It will also be valuable to data scientists seeking mathematical sophistication. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Hardcover. Condizione: new. Excellent Condition.Excels in customer satisfaction, prompt replies, and quality checks.

Condizione: New. In.

Condizione: New.

Condizione: New.

Condizione: New.

Condizione: New.

Paperback. Condizione: new. Paperback. This text provides deep and comprehensive coverage of the mathematical background for data science, including machine learning, optimal recovery, compressed sensing, optimization, and neural networks. In the past few decades, heuristic methods adopted by big tech companies have complemented existing scientific disciplines to form the new field of Data Science. This text embarks the readers on an engaging itinerary through the theory supporting the field. Altogether, twenty-seven lecture-length chapters with exercises provide all the details necessary for a solid understanding of key topics in data science. While the book covers standard material on machine learning and optimization, it also includes distinctive presentations of topics such as reproducing kernel Hilbert spaces, spectral clustering, optimal recovery, compressed sensing, group testing, and applications of semidefinite programming. Students and data scientists with less mathematical background will appreciate the appendices that provide more background on some of the more abstract concepts. This text explores a diverse set of data science topics through a mathematical lens, helping mathematicians become acquainted with data science in general, and machine learning, optimal recovery, compressive sensing, optimization, and neural networks in particular. It will also be valuable to data scientists seeking mathematical sophistication. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Condizione: As New. Unread book in perfect condition.

Condizione: As New. Unread book in perfect condition.

Hardcover. Condizione: new. Hardcover. This text provides deep and comprehensive coverage of the mathematical background for data science, including machine learning, optimal recovery, compressed sensing, optimization, and neural networks. In the past few decades, heuristic methods adopted by big tech companies have complemented existing scientific disciplines to form the new field of Data Science. This text embarks the readers on an engaging itinerary through the theory supporting the field. Altogether, twenty-seven lecture-length chapters with exercises provide all the details necessary for a solid understanding of key topics in data science. While the book covers standard material on machine learning and optimization, it also includes distinctive presentations of topics such as reproducing kernel Hilbert spaces, spectral clustering, optimal recovery, compressed sensing, group testing, and applications of semidefinite programming. Students and data scientists with less mathematical background will appreciate the appendices that provide more background on some of the more abstract concepts. This text explores a diverse set of data science topics through a mathematical lens, helping mathematicians become acquainted with data science in general, and machine learning, optimal recovery, compressive sensing, optimization, and neural networks in particular. It will also be valuable to data scientists seeking mathematical sophistication. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Condizione: New. New edition NO-PA16APR2015-KAP.

Buch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - A diverse selection of data science topics explored through a mathematical lens.

Condizione: New.

Hardcover. Condizione: new. Hardcover. This text provides deep and comprehensive coverage of the mathematical background for data science, including machine learning, optimal recovery, compressed sensing, optimization, and neural networks. In the past few decades, heuristic methods adopted by big tech companies have complemented existing scientific disciplines to form the new field of Data Science. This text embarks the readers on an engaging itinerary through the theory supporting the field. Altogether, twenty-seven lecture-length chapters with exercises provide all the details necessary for a solid understanding of key topics in data science. While the book covers standard material on machine learning and optimization, it also includes distinctive presentations of topics such as reproducing kernel Hilbert spaces, spectral clustering, optimal recovery, compressed sensing, group testing, and applications of semidefinite programming. Students and data scientists with less mathematical background will appreciate the appendices that provide more background on some of the more abstract concepts. This text explores a diverse set of data science topics through a mathematical lens, helping mathematicians become acquainted with data science in general, and machine learning, optimal recovery, compressive sensing, optimization, and neural networks in particular. It will also be valuable to data scientists seeking mathematical sophistication. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Hardcover. Condizione: new. Hardcover. This text provides deep and comprehensive coverage of the mathematical background for data science, including machine learning, optimal recovery, compressed sensing, optimization, and neural networks. In the past few decades, heuristic methods adopted by big tech companies have complemented existing scientific disciplines to form the new field of Data Science. This text embarks the readers on an engaging itinerary through the theory supporting the field. Altogether, twenty-seven lecture-length chapters with exercises provide all the details necessary for a solid understanding of key topics in data science. While the book covers standard material on machine learning and optimization, it also includes distinctive presentations of topics such as reproducing kernel Hilbert spaces, spectral clustering, optimal recovery, compressed sensing, group testing, and applications of semidefinite programming. Students and data scientists with less mathematical background will appreciate the appendices that provide more background on some of the more abstract concepts. This text explores a diverse set of data science topics through a mathematical lens, helping mathematicians become acquainted with data science in general, and machine learning, optimal recovery, compressive sensing, optimization, and neural networks in particular. It will also be valuable to data scientists seeking mathematical sophistication. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Paperback. Condizione: Brand New. 350 pages. 9.00x6.00x0.71 inches. In Stock. This item is printed on demand.

Paperback / softback. Condizione: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days 510.