Amazon.co.jp: 機械学習のための連続最適化 (機械学習プロフェッショナルシリーズ): 金森敬文, 鈴木大慈, 竹内一郎, 佐藤一誠: 本
Hiroshi Takahashi
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Adam — latest trends in deep learning optimization.
Adam [1] is an adaptive learning rate optimization algorithm that’s been designed specifically for training deep neural networks. First published in 2014, Adam was presented at a very prestigious conference for deep learning practitioners — ICLR 2015. The paper contained some very promising diagrams, showing huge performance gains in terms of speed of training. However, after a while people starte
Adam: A Method for Stochastic Optimization
We 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 requirements, is invariant to diagonal rescaling of the gradients, and is well suited for problems that are large in terms of data and/or paramet
Stochastic gradient descent - Wikipedia
Stochastic gradient descent (often abbreviated SGD) is an iterative method for optimizing an objective function with suitable smoothness properties (e.g. differentiable or subdifferentiable). It can be regarded as a stochastic approximation of gradient descent optimization, since it replaces the actual gradient (calculated from the entire data set) by an estimate thereof (calculated from a randoml
Hyperparameter optimization - Wikipedia
In machine learning, hyperparameter optimization[1] or tuning is the problem of choosing a set of optimal hyperparameters for a learning algorithm. A hyperparameter is a parameter whose value is used to control the learning process, which must be configured before the process starts.[2] Hyperparameter optimization determines the set of hyperparameters that yields an optimal model which minimizes a
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Heuristic (computer science) - Wikipedia
In mathematical optimization and computer science, heuristic (from Greek εὑρίσκω "I find, discover"[1]) is a technique designed for problem solving more quickly when classic methods are too slow for finding an exact or approximate solution, or when classic methods fail to find any exact solution in a search space. This is achieved by trading optimality, completeness, accuracy, or precision for spe
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