added rnn code in python

Python/recurrent_neural_network.py

@@ -0,0 +1,64 @@
+"""
+Minimal Recurrent Neural Network (RNN) demonstration.
+
+Forward propagation explanation:
+https://towardsdatascience.com/forward-propagation-in-neural-networks-simplified-math-and-code-version-bbcfef6f9250
+RNN fundamentals:
+https://towardsdatascience.com/recurrent-neural-networks-d4642c9bc7ce/
+"""
+
+import math
+import random
+
+
+def sigmoid_function(value: float, deriv: bool = False) -> float:
+    if deriv:
+        return value * (1 - value)
+    return 1 / (1 + math.exp(-value))
+
+
+# Initial constants
+INITIAL_VALUE = 0.02  # learning rate
+SEQUENCE_LENGTH = 5  # time steps in the sequence
+
+
+def forward_propagation_rnn(expected: int, number_propagations: int) -> float:
+    random.seed(0)
+
+    # Random weight initialization
+    w_xh = random.random() * 2 - 1  
+    w_hh = random.random() * 2 - 1  
+    w_hy = random.random() * 2 - 1  
+
+    # Training loop
+    for _ in range(number_propagations):
+        h_prev = 0.0  
+        total_error = 0.0
+
+        # Forward pass through time
+        for _t in range(SEQUENCE_LENGTH):
+
+            x_t = INITIAL_VALUE
+            h_t = sigmoid_function(w_xh * x_t + w_hh * h_prev)
+            y_t = sigmoid_function(w_hy * h_t)
+            error_t = (expected / 100) - y_t
+            total_error += abs(error_t)
+
+            # Backpropagation Through Time 
+            d_y = error_t * sigmoid_function(y_t, True)
+            d_h = d_y * w_hy * sigmoid_function(h_t, True)
+
+            w_hy += INITIAL_VALUE * d_y * h_t
+            w_xh += INITIAL_VALUE * d_h * x_t
+            w_hh += INITIAL_VALUE * d_h * h_prev
+
+            h_prev = h_t
+
+    final_output = y_t * 100
+    return final_output
+
+
+if __name__ == "__main__":
+    expected = 50
+    number_propagations = 100
+    print(forward_propagation_rnn(expected, number_propagations))