| 청구기호 |
Q325.73 .B464 2022eb |
| 형태사항 |
1 online resource (various pagings) : illustrations (some color).
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| 언어 |
English |
| 일반주기 |
"Version: 20221001"--Title page verso.
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| 서지주기 |
Includes bibliographical references.
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| 내용 |
part I. Introduction. 1. Deep learning for social good--an introduction -- 1.1. Deep learning--a subset of AI -- 1.2. History of deep learning -- 1.3. Trends--deep learning for social good -- 1.4. Motivations -- 1.5. Deep learning for social good--a need -- 1.6. Intended audience -- 1.7. Chapters and descriptions -- 1.8. Reading flow
2. Applications for social good -- 2.1. Characteristics of social-good applications -- 2.2. Generic architecture--entities -- 2.3. Applications for social good -- 2.4. Technologies and techniques -- 2.5. Technology--blockchain -- 2.6. AI/machine learning/deep learning techniques -- 2.7. The Internet of things/sensor technology -- 2.8. Robotic technology -- 2.9. Computing infrastructures--a needy technology -- 2.10. Security-related techniques
3. Computing architectures--base technologies -- 3.1. History of computing -- 3.2. Types of computing -- 3.3. Hardware support for deep learning -- 3.4. Microcontrollers, microprocessors, and FPGAs -- 3.5. Cloud computing--an environment for deep learning -- 3.6. Virtualization--a base for cloud computing -- 3.7. Hypervisors--impact on deep learning -- 3.8. Containers and Dockers -- 3.9. Cloud execution models -- 3.10. Programming deep learning tasks--libraries -- 3.11. Sensor-enabled data collection for DLs -- 3.12. Edge-level deep learning systems
part II. Deep learning techniques. 4. CNN techniques -- 4.1. CNNs--introduction -- 4.2. CNNs--nuts and bolts -- 4.3. Social-good applications--a CNN perspective -- 4.4. CNN use case--climate change problem -- 4.5. CNN challenges
5. Object detection techniques and algorithms -- 5.1. Computer vision--taxonomy -- 5.2. Object detection--objectives -- 5.3. Object detection--challenges -- 5.4. Object detection--major steps or processes -- 5.5. Object detection methods -- 5.6. Applications -- 5.7. Exam proctoring--YOLOv5 -- 5.8. Proctoring system--implementation stages
6. Sentiment analysis--algorithms and frameworks -- 6.1. Sentiment analysis--an introduction -- 6.2. Levels and approaches -- 6.3. Sentiment analysis--processes -- 6.4. Recommendation system--sentiment analysis -- 6.5. Movie recommendation--a case study -- 6.6. Metrics -- 6.7. Tools and frameworks -- 6.8. Sentiment analysis--sarcasm detection
7. Autoencoders and variational autoencoders -- 7.1. Introduction--autoencoders -- 7.2. Autoencoder architectures -- 7.3. Types of autoencoder -- 7.4. Applications of autoencoders -- 7.5. Variational autoencoders -- 7.6. Autoencoder implementation--code snippet explanation
8. GANs and disentangled mechanisms -- 8.1. Introduction to GANs -- 8.2. Concept--generative and descriptive -- 8.3. Major steps involved -- 8.4. GAN architecture -- 8.5. Types of GAN -- 8.6. StyleGAN -- 8.7. A simple implementation of a GAN -- 8.8. Quality of GANs -- 8.9. Applications and challenges
9. Deep reinforcement learning architectures -- 9.1. Deep reinforcement learning--an introduction -- 9.2. The difference between deep reinforcement learning and machine learning -- 9.3. The difference between deep learning and reinforcement learning -- 9.4. Reinforcement learning applications -- 9.5. Components of RL frameworks -- 9.6. Reinforcement learning techniques -- 9.7. Reinforcement learning algorithms -- 9.8. Integration into real-world systems
10. Facial recognition and applications -- 10.1. Facial recognition--a historical view -- 10.2. Biometrics using faces -- 10.3. Facial detection versus recognition -- 10.4. Facial recognition--processes -- 10.5. Applications -- 10.6. Emotional intelligence--a facial recognition application -- 10.7. Emotion detection--database creation -- 10.8. Challenges and future work
part III. Security, performance, and future directions. 11. Data security and platforms -- 11.1. Security breaches -- 11.2. Security attacks -- 11.3. Deep-learning-related security attacks -- 11.4. Metrics -- 11.5. Execution environments -- 11.6. Using deep learning to enhance security
12. Performance monitoring and analysis -- 12.1. Performance monitoring -- 12.2. The need for performance monitoring -- 12.3. Performance analysis methods/approaches -- 12.4. Performance metrics -- 12.5. Evaluation platforms
13. Deep learning--future perspectives -- 13.1. Data diversity and generalization -- 13.2. Applications.
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| 주제 |
Deep learning (Machine learning)
Technology --Social aspects.
Neural networks & fuzzy systems. --bicssc
Engineering. --bisacsh
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| 보유판 및 특별호 저록 |
Print version: 9780750340229 9780750340250
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| ISBN |
9780750340243qebook
9780750340236 |
| 기타 표준번호 |
10.1088/978-0-7503-4024-3 |
