Pocket Primer Series Read Description

SVG

Pocket Primer

Paperback
October 2016
9781944534592
More details
  • Publisher
    Mercury Learning and Information
  • Published
    6th October 2016
  • ISBN 9781944534592
  • Language English
  • Pages 200 pp.
  • Size 6" x 9"
$29.95
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October 2016
9781683923213
More details
  • Publisher
    Mercury Learning and Information
  • Published
    6th October 2016
  • ISBN 9781683923213
  • Language English
  • Pages 200 pp.
  • Size 6" x 9"
$74.95
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September 2016
9781944534691
More details
  • Publisher
    Mercury Learning and Information
  • Published
    15th September 2016
  • ISBN 9781944534691
  • Language English
  • Pages 200 pp.
  • Size 6" x 9"
$19.95

As part of the Pocket Primer series, this book provides an overview of the major aspects and the source code to use SVG. This Pocket Primer is primarily for self-directed learners who want to learn SVG and it serves as a starting point for deeper exploration of its programming.

Features:

• Includes companion files with all of the source code and images from the book
• Contains material devoted to SVG gradients and filters, graphics, animation, etc., use with CSS3, D3, Angular2, and covers SVG application programming interfaces and other toolkits
• Provides a solid introduction to SVG via complete code samples and images

Companion Files:

• Source code samples
• All images from the text (including 4-color)

1. Introduction to SVG.
2. SVG Gradients and Filters.
3. SVG Transforms.
4. Ellipses, Arcs , and Bezier Curves.
5. Introduction to CSS3 Graphics and Animation.
6. Introduction to D3.
7. Mouse Events and Animation Effects.
8. Data Visualization.
9. Designing Mobile Apps.
10. Miscellaneous Topics: SVG Utilities and IDEs.
Appendix: Graphics with ReactJS and Angular 2.
Index.

Oswald Campesato

Oswald Campesato (San Francisco, CA) is an adjunct instructor at UC-Santa Clara and specializes in Deep Learning, Java, Android, TensorFlow, and NLP. He is the author/co-author of over twenty-five books including TensorFlow 2 Pocket Primer, Python 3 for Machine Learning, and the NLP Using R Pocket Primer (all Mercury Learning and Information).