misc updates

Author: lazyprogrammer

README.md

@@ -55,3 +55,6 @@ https://deeplearningcourses.com/c/deep-learning-recurrent-neural-networks-in-pyt
 
 Advanced Natural Language Processing: Deep Learning in Python
 https://deeplearningcourses.com/c/natural-language-processing-with-deep-learning-in-python
+
+Artificial Intelligence: Reinforcement Learning in Python
+https://deeplearningcourses.com/c/artificial-intelligence-reinforcement-learning-in-python

ann_class/forwardprop.py

@@ -29,8 +29,11 @@
 W2 = np.random.randn(M, K)
 b2 = np.random.randn(K)
 
+def sigmoid(a):
+    return 1 / (1 + np.exp(-a))
+
 def forward(X, W1, b1, W2, b2):
-    Z = 1 / (1 + np.exp(-X.dot(W1) - b1))
+    Z = sigmoid(X.dot(W1) + b1)
     A = Z.dot(W2) + b2
     expA = np.exp(A)
     Y = expA / expA.sum(axis=1, keepdims=True)

ann_class2/tensorflow2.py

@@ -70,7 +70,7 @@ def main():
     # softmax_cross_entropy_with_logits take in the "logits"
     # if you wanted to know the actual output of the neural net,
     # you could pass "Yish" into tf.nn.softmax(logits)
-    cost = tf.reduce_sum(tf.nn.softmax_cross_entropy_with_logits(Yish, T))
+    cost = tf.reduce_sum(tf.nn.softmax_cross_entropy_with_logits(logits=Yish, labels=T))
 
     # we choose the optimizer but don't implement the algorithm ourselves
     # let's go with RMSprop, since we just learned about it.