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Скачать с ютуб Neural Networks with different Activation Functions Visualized (ReLU, GELU, Tanh, ...) в хорошем качестве

Neural Networks with different Activation Functions Visualized (ReLU, GELU, Tanh, ...) 4 года назад


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Neural Networks with different Activation Functions Visualized (ReLU, GELU, Tanh, ...)

Visualized of Regression and Classification NeuralNets with different activation functions (ReLU, GELU, Tanh, Hardshrink, Tanhshrink). The architecture of the model is given as sequence of Linear (aka Dense) Layers. The first value defines the number of input neurons, while the second defines the number of output neurons. E.g.: Linear(2, 64) → Linear(64, 3) defines a Neural Net with 2 input neurons, 1 hidden layer with 64 neurons and 3 output neurons. Activations are applied after each layer, except the last one, which has no activation (regression) or sigmoid/softmax activation (classification) The gradient mean and std is calculated from the gradient of the loss w.r.t the input. Time stamps 00:00 Intro 00:05 Regression Sine Function 01:25 Regression Circular Function 02:45 Binary Classification 04:05 Multi-Class Classification

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