completed part 1 of deep learning

python/Deep Learning/Introduction to Neural Networks/Softmax/cross_entropy.py

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+import numpy as np
+
+# Write a function that takes as input two lists Y, P,
+# and returns the float corresponding to their cross-entropy.
+
+
+def cross_entropy(Y, P):
+    Y = np.float_(Y)
+    P = np.float_(P)
+    return -np.sum(Y * np.log(P) + (1 - Y) * np.log(1 - P))

python/Deep Learning/Introduction to Neural Networks/Softmax/softmax.py

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+import numpy as np
+
+# Write a function that takes as input a list of numbers, and returns
+# the list of values given by the softmax function.
+
+
+def softmax(L):
+    expL = np.exp(L)
+    sumExpL = sum(expL)
+    result = []
+    for i in expL:
+        result.append(i * 1.0 / sumExpL)
+    return result
+
+    # Note: The function np.divide can also be used here, as follows:
+    # def softmax(L):
+    #     expL = np.exp(L)
+    #     return np.divide (expL, expL.sum())