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Applying Deep Learning in Business /
According to a recent poll conducted by O'Reilly Media, most data scientists already know what AI technologies, such as deep learning, can do. Now they want to learn how to implement neural networks and deep learning to address their unique business objective. They're looking for business...
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| Formato: | Electrónico eBook |
| Idioma: | Indeterminado |
| Publicado: |
[Place of publication not identified]
O'Reilly Media, Inc.,
2019.
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| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 520 | |a According to a recent poll conducted by O'Reilly Media, most data scientists already know what AI technologies, such as deep learning, can do. Now they want to learn how to implement neural networks and deep learning to address their unique business objective. They're looking for business use cases, real-world examples, and tutorials and tips for overcoming challenges with these projects. And they're seeking a Cloud-based service so they can spin up a service in matter of minutes and only pay for what they use. With tools such as Deep Learning as a Service within IBM Watson Studio, building and deploying deep learning models in the enterprise is getting easier. This practical report provides enterprise application developers with specific use cases and steps for implementation, data scientist Federico Castanedo provides readers with a foundational understanding of deep learning and demonstrates how companies are using it in their business today. You'll learn two approaches to implementing deep learning in your organization: build and train your own deep learning models, or leverage pre-trained models. Learn what deep learning can do in the enterprise Understand the general process of building and training neural networks in-house for deep learning projects Contrast building your own solution with using and deploying pre-built models Design deep learning models in the cloud with IBM Watson Studio and popular frameworks such as TensorFlow, Caffe, PyTorch and Keras. | ||
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