Customizing state-of-the-art deep learning models for new computer vision solutions
Dramatic progress has been made in computer vision: deep neural networks (DNNs) trained on tens of millions of images can now recognize thousands of different object types. These DNNs can also be easily customized to new use cases. Timothy Hazen shares simple methods and tools that enable you to ada...
| Otros Autores: | , , , |
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| Formato: | |
| Idioma: | Inglés |
| Publicado: |
O'Reilly Media, Inc
2018.
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| Edición: | 1st edition |
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| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009631303106719 |
| Sumario: | Dramatic progress has been made in computer vision: deep neural networks (DNNs) trained on tens of millions of images can now recognize thousands of different object types. These DNNs can also be easily customized to new use cases. Timothy Hazen shares simple methods and tools that enable you to adapt Microsoft's state-of-the-art DNNs for use in your own computer vision solutions. |
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| Notas: | Title from title screen (viewed July 16, 2018). |
| Descripción Física: | 1 online resource (1 video file, approximately 37 min.) |
