In ANN the equation during Forward Propagation is `Y = W.X + b`

.

What is the equation during Forward Propagation for RNN, as it involves `States`

and `Timesteps`

.

What is the difference between `ANN`

and `RNN`

in terms of Back Propagation.

Also, what is the difference in functionality between `Dropout`

in ANN vs `Recurrent_Dropout`

in RNN.

Are there any other key differences between `ANN`

and `RNN`

.

The equation for Forward Propagation of RNN, considering

`Two Timesteps`

, in a simple form, is shown below:Output of the First Time Step:`Y0 = (Wx * X0) + b)`

Output of the Second Time Step:`Y1 = (Wx * X1) + Y0 * Wy + b`

where`Y0 = (Wx * X0) + b)`

To elaborate it, consider

`RNN`

has 5`Neurons/Units`

, more detailed equation is mentioned in the screenshot below:Equation of Forward Propagation of RNN

RNN中的反向传播：

`cost`

function`C(y(t(min)), y(t(min+1)), ... y(t(max)))`

(where`tmin`

and`tmax`

are the first and last output time steps, not counting the ignored outputs), and the gradients of that cost function are propagated backward through the unrolled networkIn the screenshot below, Dashed Lines represents

`Forward Propagation`

and Solid Lines represents`Back Propagation`

.Flow of Forward Propagation and Back Propagation in RNN

Dropout: If we set the value of`Dropout`

as`0.1`

in a`Recurrent Layer`

(LSTM), it means that it will pass only 90% of Inputs to the Recurrent LayerRecurrent DroputIf we set the value of`Recurrent Dropout`

as`0.2`

in a`Recurrent Layer`

(LSTM), it means that it will consider only 80% of the Time Steps for that Recurrent Layer希望这能回答您的所有疑问！