Differentiable programming in machine learning
This paper explains automatic differentiation, discussing two primary modes - forward and backward - and their respective implementation methods. In the context of issues encountered in machine learning and deep learning, the forward mode is deemed more suitable as it efficiently differentiates func...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Savez inženjera i tehničara Srbije
2023-01-01
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| Series: | Tehnika |
| Subjects: | |
| Online Access: | https://scindeks-clanci.ceon.rs/data/pdf/0040-2176/2023/0040-21762306699K.pdf |
| Summary: | This paper explains automatic differentiation, discussing two primary modes - forward and backward - and their respective implementation methods. In the context of issues encountered in machine learning and deep learning, the forward mode is deemed more suitable as it efficiently differentiates functions with numerous inputs compared to outputs. Given Python's pivotal role in the ML landscape, the paper elaborates on two widely used deep learning libraries-PyTorch and TensorFlow. While both these libraries support automatic differentiation, they adopt distinct approaches, each carrying its unique strengths and weaknesses. |
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| ISSN: | 0040-2176 2560-3086 |