Changes learning rate description (#1546)
* Fixes learning rate and momentum definitions Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Update algorithms.md * Makes epsilon, beta1, beta2 and decayRate definitions uniform Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Changes learning rate description Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> Signed-off-by: Fanit Kolchina <kolchfa@amazon.com>
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@ -65,7 +65,7 @@ Linear regression maps the linear relationship between inputs and outputs. In ML
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Parameter | Type | Description | Default Value
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:--- |:--- | :--- | :---
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learningRate | Double | The gradient descent step size at each iteration when moving toward a minimum of a loss function or optimal value. | 0.01
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learningRate | Double | The initial step size used in an iterative optimization algorithm. | 0.01
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momentumFactor | Double | The extra weight factors that accelerate the rate at which the weight is adjusted. This helps move the minimization routine out of local minima. | 0
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epsilon | Double | The value for stabilizing gradient inversion. | 1.00E-06
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beta1 | Double | The exponential decay rates for the moment estimates. | 0.9
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@ -413,7 +413,7 @@ A classification algorithm, logistic regression models the probability of a disc
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| Parameter | Type | Description | Default Value |
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|---|---|---|---|
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| learningRate | Double | The gradient descent step size at each iteration when moving toward a minimum of a loss function or optimal value | 1 |
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| learningRate | Double | The initial step size used in an iterative optimization algorithm. | 1 |
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| momentumFactor | Double | The extra weight factors that accelerate the rate at which the weight is adjusted. This helps move the minimization routine out of local minima. | 0 |
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| epsilon | Double | The value for stabilizing gradient inversion. | 0.1 |
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| beta1 | Double | The exponential decay rates for the moment estimates. | 0.9 |
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