Meta-Learning: Theory, Algorithms and Applications

Book Description

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.

中文：

书名：元学习: 理论、算法和应用

元学习: 概述介绍了元学习的基本原理，理解了学习的概念。在给出了人工智能、机器学习、深度强化学习和元学习的背景后，它为元学习提供了重要的最先进机制，包括记忆增强神经网络、元网络、卷积暹罗神经网络、匹配网络、原型网络、关系网络、LSTM元学习、模型不可知元学习和爬行动物。然后演示了元学习的原理和算法在计算机视觉，元强化学习，机器人技术，语音识别，自然语言处理，金融，业务管理和医疗保健中的应用。最后一章总结了挑战、机遇和未来趋势。

元学习: 概述让学生和研究人员了解元学习的原理和最新的元学习算法，使元学习能够在一系列应用中使用。