[Few-shot learning][2.4] MAML: Model-Agnostic Meta-Learning скачать в хорошем качестве

[Few-shot learning][2.4] MAML: Model-Agnostic Meta-Learning

In this episode I am giving an overview of MAML (Model-Agnostic Meta-Learning) which has been introduced in 2017 at ICML. I provide a step-by-step explanation of the algorithm and an overview of the pytorch implementation. MAML is particularly interesting because it allows estimating a set of generic meta-parameters which can be rapidly adapted to solve specific tasks. This can be done in a fully differentiable way. Paper: https://arxiv.org/pdf/1703.03400.pdf GitHub (tensorflow): https://github.com/cbfinn/maml GitHub (pytorch): https://github.com/tristandeleu/pytor... ____________________________________________________________________ My Blog: https://mpatacchiola.github.io/blog/ GitHub: https://github.com/mpatacchiola Linkedin:   / massimiliano-patacchiola-94579b140   #machinelearning #deeplearning #metalearning #MAML #fewshotlearning #neuralnetworks