Deep learning from scratch building with Python from first principles
With the resurgence of neural networks in the 2010s, understanding deep learning has become essential for machine learning practitioners and even many software engineers. This practical book provides a thorough introduction for data scientists and software engineers with previous exposure to machin...
| Other Authors: | |
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| Format: | eBook |
| Language: | Inglés |
| Published: |
Beijing :
O'Reilly
[2019]
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| Edition: | First edition |
| Subjects: | |
| See on Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009630764806719 |
| Summary: | With the resurgence of neural networks in the 2010s, understanding deep learning has become essential for machine learning practitioners and even many software engineers. This practical book provides a thorough introduction for data scientists and software engineers with previous exposure to machine learning. You'll start with deep learning basics and move quickly to the details of important advanced architectures, implementing everything from scratch along the way. Author Seth Weidman shows you how neural networks function using a first principles approach. You'll learn how to apply multilayer neural networks, convolutional neural networks, and recurrent neural networks from the ground up. With a detailed understanding of how these networks work mathematically, computationally, and conceptually, you'll be set up for success on future deep learning projects. |
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| Physical Description: | (253 páginas ; 24 cm |
| Bibliography: | Bibliografía |
| ISBN: | 9781492041368 9781492041405 9781492041382 |
