Hands-On Meta Learning with Python explains the fundamentals of meta learning and helps you to understand the concept of learning to learn. You will go through various one-shot learning algorithms, such as siamese, prototypical, relation, and memory-augmented networks, and implement them in Tensor...
But how can we check the performance of and how can we update our meta learner parameter? We take the test set and compute the loss on our test set with parameter . Then, we calculate the gradients of the loss with respect to our meta learner parameter and then we update , as shown ...
Hands-On Meta Learning with Python上QQ阅读APP,阅读体验更流畅 领看书特权 Algorithm The algorithm of the prototypical networks is shown here: Let's say we have the dataset, D, comprising {(x1, y1), (x2, y2), ... (xn, yn)} where x is the feature and y is the class label. ...
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Metalearningisanexcitingresearchtrendinmachinelearning,whichenablesamodeltounderstandthelearningprocess.UnlikeotherMLparadigms,withmetalearningyoucanlearnfromsmalldatasetsfaster.Hands-OnMetaLearningwithPythonstartsbyexplainingthefundamentalsofmetalearningandhelpsyouunderstandtheconceptoflearningtolearn.Youwilldelveintovarious...
Hands-On Meta Learning With Python Learning to Learn using One-Shot Learning, MAML, Reptile, Meta-SGD and more About the book Meta learning is an exciting research trend in machine learning, which enables a model to understand the learning process. Unlike other ML paradigms, with meta learning...
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