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Understanding convolutional neural networks (CNNs) : learn how to implement CNNs to generate visualizations /
"Convolutional neural networks (CNNs) enable very powerful deep learning based techniques for processing, generating, and sensemaking of visual information. These are revolutionary techniques in computer vision that impact technologies ranging from e-commerce to self-driving cars. This course o...
| Clasificación: | Libro Electrónico |
|---|---|
| Otros Autores: | |
| Formato: | Electrónico Video |
| Idioma: | Inglés |
| Publicado: |
[Place of publication not identified] :
O'Reilly,
[2017]
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| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 037 | |a CL0500000884 |b Safari Books Online | ||
| 050 | 4 | |a QA76.87 | |
| 049 | |a UAMI | ||
| 100 | 1 | |a Watson, Nell, |e speaker. | |
| 245 | 1 | 0 | |a Understanding convolutional neural networks (CNNs) : |b learn how to implement CNNs to generate visualizations / |c with Nell Watson. |
| 264 | 1 | |a [Place of publication not identified] : |b O'Reilly, |c [2017] | |
| 300 | |a 1 online resource (1 streaming video file (1 hr., 48 min., 8 sec.)) | ||
| 336 | |a two-dimensional moving image |b tdi |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 337 | |a video |b v |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 511 | 0 | |a Presenter, Nell Watson. | |
| 500 | |a Title from title screen (viewed August 15, 2017). | ||
| 500 | |a Date of publication from resource description page. | ||
| 520 | |a "Convolutional neural networks (CNNs) enable very powerful deep learning based techniques for processing, generating, and sensemaking of visual information. These are revolutionary techniques in computer vision that impact technologies ranging from e-commerce to self-driving cars. This course offers an in-depth examination of CNNs, their fundamental processes, their applications, and their role in visualization and image enhancement. The course covers concepts, processes, and technologies such as CNN layers and architectures. It also explains CNN image classification and segmentation, deep dream and style transfer, super-resolution, and generative adversarial networks (GANs). Learners who come to this course with a basic knowledge of deep learning principles, some computer vision experience, and exposure to engineering math should gain the ability to implement CNNs and use them to create their own visualizations."--Resource description page | ||
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Neural networks (Computer science) | |
| 650 | 0 | |a Computer vision. | |
| 650 | 0 | |a Machine learning. | |
| 650 | 2 | |a Neural Networks, Computer | |
| 650 | 6 | |a Réseaux neuronaux (Informatique) | |
| 650 | 6 | |a Vision par ordinateur. | |
| 650 | 6 | |a Apprentissage automatique. | |
| 650 | 7 | |a Computer vision. |2 fast |0 (OCoLC)fst00872687 | |
| 650 | 7 | |a Machine learning. |2 fast |0 (OCoLC)fst01004795 | |
| 650 | 7 | |a Neural networks (Computer science) |2 fast |0 (OCoLC)fst01036260 | |
| 710 | 2 | |a O'Reilly & Associates, |e publisher. | |
| 856 | 4 | 0 | |u https://learning.oreilly.com/videos/~/9781491978931/?ar |z Texto completo (Requiere registro previo con correo institucional) |
| 994 | |a 92 |b IZTAP | ||