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200215s2020 cau b 001 0 eng c
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▼z 9781484251249 (ebk.)
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▼z 1484251245 (ebk.)
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▼a 9781484251232 (pbk.)
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▼a 243002!!
▼c 243002
▼d 243002 -
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▼a 006.31
▼b P184p -
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▼a Paluszek, Michael.
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▼a Practical MATLAB deep learning:
▼b a project-based approach/
▼d Michael Paluszek,
▼e Stephanie Thomas. -
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▼a Berkeley, CA:
▼b Apress,
▼c 2020. -
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▼a 260 p.;
▼c 26 cm. -
500
▼a Description based upon print version of record.
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500
▼a 6.6.3 How It Works
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▼a Includes bibliographical references and index.
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505
▼a Intro -- Contents -- About the Authors -- About the Technical Reviewer -- Acknowledgements -- 1 What Is Deep Learning? -- 1.1 Deep Learning -- 1.2 History of Deep Learning -- 1.3 Neural Nets -- 1.3.1 Daylight Detector -- Problem -- Solution -- How It Works -- 1.3.2 XOR Neural Net -- Problem -- Solution -- How It Works -- 1.4 Deep Learning and Data -- 1.5 Types of Deep Learning -- 1.5.1 Multilayer Neural Network -- 1.5.2 Convolutional Neural Networks (CNN) -- 1.5.3 Recurrent Neural Network (RNN) -- 1.5.4 Long Short-Term Memory Networks (LSTMs) -- 1.5.5 Recursive Neural Network
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520
▼a Harness the power of MATLAB for deep-learning challenges. This book provides an introduction to deep learning and using MATLAB's deep-learning toolboxes. Youll see how these toolboxes provide the complete set of functions needed to implement all aspects of deep learning. Along the way, you'll learn to model complex systems, including the stock market, natural language, and angles-only orbit determination. Youll cover dynamics and control, and integrate deep-learning algorithms and approaches using MATLAB. You'll also apply deep learning to aircraft navigation using images. Finally, you'll carry out classification of ballet pirouettes using an inertial measurement unit to experiment with MATLAB's hardware capabilities. You will: Explore deep learning using MATLAB and compare it to algorithms Write a deep learning function in MATLAB and train it with examples Use MATLAB toolboxes related to deep learning Implement tokamak disruption prediction.
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630
▼a MATLAB.
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650
▼a Machine learning.
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700
▼a Thomas, Stephanie.
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▼a 송미영
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991
▼a 단행본
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상세정보
Practical MATLAB deep learning : a project-based approach
QR코드| 자료유형 : | 단행본 |
|---|---|
| ISBN : | |
| ISBN : | |
| ISBN : | 9781484251232 (pbk.) |
| 분류기호 : | 006.31 |
| 개인저자 : | Paluszek, Michael. |
| 서명/저자사항 : | Practical MATLAB deep learning: a project-based approach/ Michael Paluszek, Stephanie Thomas. |
| 발행사항 : | Berkeley, CA: Apress, 2020. |
| 형태사항 : | 260 p.; 26 cm. |
| 일반주기 : | Description based upon print version of record. |
| 일반주기 : | 6.6.3 How It Works |
| 서지주기 : | Includes bibliographical references and index. |
| 내용주기 : | Intro -- Contents -- About the Authors -- About the Technical Reviewer -- Acknowledgements -- 1 What Is Deep Learning? -- 1.1 Deep Learning -- 1.2 History of Deep Learning -- 1.3 Neural Nets -- 1.3.1 Daylight Detector -- Problem -- Solution -- How It Works -- 1.3.2 XOR Neural Net -- Problem -- Solution -- How It Works -- 1.4 Deep Learning and Data -- 1.5 Types of Deep Learning -- 1.5.1 Multilayer Neural Network -- 1.5.2 Convolutional Neural Networks (CNN) -- 1.5.3 Recurrent Neural Network (RNN) -- 1.5.4 Long Short-Term Memory Networks (LSTMs) -- 1.5.5 Recursive Neural Network |
| 요약 : | Harness the power of MATLAB for deep-learning challenges. This book provides an introduction to deep learning and using MATLAB's deep-learning toolboxes. Youll see how these toolboxes provide the complete set of functions needed to implement all aspects of deep learning. Along the way, you'll learn to model complex systems, including the stock market, natural language, and angles-only orbit determination. Youll cover dynamics and control, and integrate deep-learning algorithms and approaches using MATLAB. You'll also apply deep learning to aircraft navigation using images. Finally, you'll carry out classification of ballet pirouettes using an inertial measurement unit to experiment with MATLAB's hardware capabilities. You will: Explore deep learning using MATLAB and compare it to algorithms Write a deep learning function in MATLAB and train it with examples Use MATLAB toolboxes related to deep learning Implement tokamak disruption prediction. |
| 주제명(통일서명) : | MATLAB. -- |
| 일반주제명 : | Machine learning. -- |
| 개인저자 : | Thomas, Stephanie. |
| 언어 | 영어 |
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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