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Meta-Learning: Theory, Algorithms and Applications Zou, Lan (Columnist, Association for the Advancement of Artificial Intelligence (https:/ / aihub.org / )<br>Researcher, AI, Silicon Valley and Carnegie Mellon University)
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Meta-Learning: Theory, Algorithms and Applications
Zou, Lan (Columnist, Association for the Advancement of Artificial Intelligence (https:/ / aihub.org / )<br>Researcher, AI, Silicon Valley and Carnegie Mellon University)
Meta-Learning: An Overview explains the fundamentals of meta-learning, giving an understanding of the concept of learning to learn. After giving a background to artificial intelligence, machine learning, deep learning, deep reinforcement learning, and meta-learning, it provides important state-of-the-art mechanisms for meta-learning, including memory-augmented neural networks, meta-networks, convolutional siamese neural networks, matching networks, prototypical networks, relation networks, LSTM meta-learning, model-agnostic meta-learning, and Reptile. It then demonstrates the application of the principles and algorithms of meta learning in computer vision, meta-reinforcement learning, robotics, speech recognition, natural language processing, finance, business management, and health care. A final chapter summarizes the challenges, opportunities and future trends.
Meta-Learning: An Overview gives students and researchers and understanding of the principles and state-of-the-art meta-learning algorithms, enabling the use of meta-learning for a range of applications.
225 pages
| Mediji | Grāmatas Paperback Book (Grāmata ar mīksto vāku un līmēto muguru) |
| Izlaists | 2022. gada 8. novembris |
| ISBN13 | 9780323899314 |
| Izdevēji | Elsevier Science & Technology |
| Lapas | 402 |
| Izmēri | 234 × 192 × 23 mm · 689 g |
| Valoda | Angļu |
| Redaktors | Zou, Lan (Columnist, Association for the Advancement of Artificial Intelligence (https:/ / aihub.org/) Researcher, AI, Silicon Valley and Carnegie Mellon University) |
