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▼a Saleh, Hyatt.
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▼a Applied Deep Learning with Pytorch:
▼b Demystify Neural Networks with Pytorch /:
▼c Hyatt Saleh.
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▼a 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.
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520
▼a 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.
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Applied Deep Learning with Pytorch : Demystify Neural Networks with Pytorch [electronic resource]
QR코드| 자료유형 : | 단행본 |
|---|---|
| ISBN : | 1789807050 |
| ISBN : | 9781789807059 |
| 개인저자 : | Saleh, Hyatt. |
| 서명/저자사항 : | Applied Deep Learning with Pytorch: Demystify Neural Networks with Pytorch /: Hyatt Saleh. [electronic resource]. |
| 발행사항 : | Birmingham: Packt Publishing, 2019. |
| 형태사항 : | 1 online resource (iv, 239 pages). |
| 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. -- |
| 일반주제명 : | Machine learning. -- |
| 기타형태 저록 : | Print version: Saleh, Hyatt. Applied Deep Learning with Pytorch : Demystify Neural Networks with Pytorch. Birmingham : Packt Publishing, Limited, ©2019, 9781789804591 |
| 언어 | 영어 |
| URL : |
|---|
예약
- 1. 예약현황은 홈페이지 로그인 후 예약 페이지에 확인 가능합니다.
- 2. 도착 통보된 예약자료 대출을 원하지 않는 경우에는 예약 현황에서 취소할 수 있습니다.
- 3. 기타 문의사항은 도서관에 문의 바랍니다.
무인예약대출
- 1. 무인예약대출 현황은 홈페이지 로그인 후 무인예약대출 페이지에 확인 가능합니다.
- 2. 무인예약대출자료 대출을 원하지 않는 경우에는 무인예약대출 페이지에서 신청 또는 접수상태인 경우만 취소할 수 있습니다.
- 3. 희망대출일은 신청일로부터 최대 1주일 까지 가능합니다.
- 4. 희망대출일을 선택하지 않은 경우 대출대기 통보 후 1주일까지 기기에서 대출가능합니다.
- 5. 기타 문의사항은 도서관에 문의 바랍니다.
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