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Deep neural networks (DNNs) with their dense and complex algorithms provide real possibilities for Artificial General Intelligence (AGI). Meta-learning with DNNs brings AGI much closer: artificial agents solving intelligent tasks that human beings can achieve, even transcending what they can achieve. Meta-Learning: Theory, Algorithms and Applications shows how meta-learning in combination with DNNs advances towards AGI. Machine Learning (ML) as a field is “concerned with the question of how to construct computer programs that automatically improve with experience.” A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E. According to the characteristics of signal and feedback, Machine Learning approaches are commonly categorized into three groups: supervised learning, unsupervised learning, and reinforcement learning