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Applied Deep Learning with Pytorch [electronic resource] Demystify Neural Networks with Pytorch
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| 자료유형 | 단행본 |
|---|---|
| 서명/저자사항 | Applied Deep Learning with Pytorch : Demystify Neural Networks with Pytorch/ Hyatt Saleh. [electronic resource]. |
| 개인저자 | Saleh, Hyatt. |
| 발행사항 | Birmingham: Packt Publishing, 2019. |
| 형태사항 | 1 online resource (iv, 239 pages). |
| 기타형태 저록 | Print version: Saleh, Hyatt. Applied Deep Learning with Pytorch : Demystify Neural Networks with Pytorch. Birmingham : Packt Publishing, Limited, ©2019 9781789804591 |
| ISBN | 1789807050 9781789807059 |
| 요약 | Starting with the basics of deep learning and their various applications, Applied Deep Learning with PyTorch shows you how to solve trending tasks, such as image classification and natural language processing by understanding the different architectures of the neural networks. |
| 요약 | Machine learning is fast becoming the preferred way to solve data problems, thanks to the huge variety of mathematical algorithms that find patterns otherwise invisible to us. Applied Deep Learning with PyTorch takes your understanding of deep learning, its algorithms, and its applications to a higher level. The book begins by helping you browse through the basics of deep learning and PyTorch. Once you are well versed with the PyTorch syntax and capable of building a single-layer neural network, you will gradually learn to tackle more complex data problems by configuring and training a convolutional neural network (CNN) to perform image classification. As you progress through the chapters, you'll discover how you can solve an NLP problem by implementing a recurrent neural network (RNN). By the end of this book, you'll be able to apply the skills and confidence you've gathered along your learning process to use PyTorch for building deep learning solutions that can solve your business data problems. |
| 일반주제명 | Machine learning. Machine learning. |
| 언어 | 영어 |
| 바로가기 |  |
