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The explanation made in the original paper [1] was that batch norm works by reducing internal covariate shift, but this has been challenged by more recent work. One experiment [2] trained a VGG-16 network [5] under 3 different training regimes: standard (no batch norm), batch norm, and batch norm with noise added to each layer during training ...
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] [3]
In machine learning, a hyperparameter is a parameter that can be set in order to define any configurable part of a model's learning process. Hyperparameters can be classified as either model hyperparameters (such as the topology and size of a neural network) or algorithm hyperparameters (such as the learning rate and the batch size of an optimizer).
In deep learning, fine-tuning is an approach to transfer learning in which the parameters of a pre-trained neural network model are trained on new data. [1] Fine-tuning can be done on the entire neural network, or on only a subset of its layers, in which case the layers that are not being fine-tuned are "frozen" (i.e., not changed during backpropagation). [2]
The norm (see also Norms) can be used to approximate the optimal norm via convex relaxation. It can be shown that the L 1 {\displaystyle L_{1}} norm induces sparsity. In the case of least squares, this problem is known as LASSO in statistics and basis pursuit in signal processing.
[3] The phrase H ∞ control comes from the name of the mathematical space over which the optimization takes place: H ∞ is the Hardy space of matrix -valued functions that are analytic and bounded in the open right-half of the complex plane defined by Re( s ) > 0; the H ∞ norm is the supremum singular value of the matrix over that space.
Arm MAP, a performance profiler supporting Linux platforms.; AppDynamics, an application performance management solution [buzzword] for C/C++ applications via SDK.; AQtime Pro, a performance profiler and memory allocation debugger that can be integrated into Microsoft Visual Studio, and Embarcadero RAD Studio, or can run as a stand-alone application.
The softmax function takes as input a vector z of K real numbers, and normalizes it into a probability distribution consisting of K probabilities proportional to the exponentials of the input numbers.