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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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_ml-commons-plugin/algorithms.md
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@ -65,7 +65,7 @@ Linear regression maps the linear relationship between inputs and outputs. In ML
Parameter | Type | Description | Default Value
:--- |:--- | :--- | :---
learningRate | Double | The gradient descent step size at each iteration when moving toward a minimum of a loss function or optimal value. | 0.01
learningRate | Double | The initial step size used in an iterative optimization algorithm. | 0.01
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
epsilon | Double | The value for stabilizing gradient inversion. | 1.00E-06
beta1 | Double | The exponential decay rates for the moment estimates. | 0.9
@ -413,7 +413,7 @@ A classification algorithm, logistic regression models the probability of a disc
| Parameter | Type | Description | Default Value |
|---|---|---|---|
| learningRate | Double | The gradient descent step size at each iteration when moving toward a minimum of a loss function or optimal value | 1 |
| learningRate | Double | The initial step size used in an iterative optimization algorithm. | 1 |
| 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 |
| epsilon | Double | The value for stabilizing gradient inversion. | 0.1 |
| beta1 | Double | The exponential decay rates for the moment estimates. | 0.9 |