We explore meta-learning’s promising applications and achievements, such as multiple trials and reinforcement learning. Meta-Model: Result of running a meta-learning algorithm. True metalearning is about encoding the initial learning algorithm in a universal programming language (e.g., on a recurrent neural network or RNN), with. The meta-learning model or meta-model can then be used to make predictions. the specific rules, coefficients, or structure learned from data. We then propose a new taxonomy that provides a more comprehensive partitioning of the space of today’s meta-learning methods. After a meta-learning algorithm is trained, it results in a meta-learning model, e.g. We first discuss definitions of meta-learning and situate them concerning related fields such as transfer learning and hyperparameter optimization. This survey describes the current landscape of meta-learning. This paradigm provides an opportunity to address many of the conventional challenges of deep learning, including data and computational bottlenecks and generalization. Unlike conventional approaches to AI, where tasks are solved from scratch using a fixed learning algorithm, meta-learning aims to improve the algorithm, considering the experience of multiple learning episodes. The field of meta-learning, or learning-to-learn, has seen a dramatic increase in interest in recent years. This article was published as a part of the Data Science Blogathon. In contrast, meta-learning learns from many related tasks a meta-learner that can learn a new task more accurately and faster with fewer examples, where the.
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