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0. Referencehttps://arxiv.org/abs/1412.6980 Adam: A Method for Stochastic OptimizationWe introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. The method is straightforward to implement, is computationally efficient, has little memory rarxiv.org1. Introduction1.1. First-order Optimizer VS Se..
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0. Referencehttps://arxiv.org/abs/1609.04747 An overview of gradient descent optimization algorithmsGradient descent optimization algorithms, while increasingly popular, are often used as black-box optimizers, as practical explanations of their strengths and weaknesses are hard to come by. This article aims to provide the reader with intuitions with regaarxiv.org1. Introduction- Gradient Descent..
