completed part 1 of deep learning

python/Deep Learning/Introduction to Neural Networks/Perceptrons as Logical Operators/perceptron_and.py

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+import pandas as pd
+
+# TODO: Set weight1, weight2, and bias
+weight1 = 1.0
+weight2 = 1.0
+bias = -1.25
+
+
+# DON'T CHANGE ANYTHING BELOW
+# Inputs and outputs
+test_inputs = [(0, 0), (0, 1), (1, 0), (1, 1)]
+correct_outputs = [False, False, False, True]
+outputs = []
+
+# Generate and check output
+for test_input, correct_output in zip(test_inputs, correct_outputs):
+    linear_combination = weight1 * \
+        test_input[0] + weight2 * test_input[1] + bias
+    output = int(linear_combination >= 0)
+    is_correct_string = 'Yes' if output == correct_output else 'No'
+    outputs.append([test_input[0], test_input[1],
+                    linear_combination, output, is_correct_string])
+
+# Print output
+num_wrong = len([output[4] for output in outputs if output[4] == 'No'])
+output_frame = pd.DataFrame(outputs, columns=[
+                            'Input 1', '  Input 2', '  Linear Combination', '  Activation Output', '  Is Correct'])
+if not num_wrong:
+    print('Nice!  You got it all correct.\n')
+else:
+    print('You got {} wrong.  Keep trying!\n'.format(num_wrong))
+print(output_frame.to_string(index=False))

python/Deep Learning/Introduction to Neural Networks/Perceptrons as Logical Operators/perceptron_not.py

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+import pandas as pd
+
+# TODO: Set weight1, weight2, and bias
+weight1 = 0
+weight2 = -1
+bias = 0.5
+
+
+# DON'T CHANGE ANYTHING BELOW
+# Inputs and outputs
+test_inputs = [(0, 0), (0, 1), (1, 0), (1, 1)]
+correct_outputs = [True, False, True, False]
+outputs = []
+
+# Generate and check output
+for test_input, correct_output in zip(test_inputs, correct_outputs):
+    linear_combination = weight1 * test_input[0] + weight2 * test_input[1] + bias
+    output = int(linear_combination >= 0)
+    is_correct_string = 'Yes' if output == correct_output else 'No'
+    outputs.append([test_input[0], test_input[1], linear_combination, output, is_correct_string])
+
+# Print output
+num_wrong = len([output[4] for output in outputs if output[4] == 'No'])
+output_frame = pd.DataFrame(outputs, columns=['Input 1', '  Input 2', '  Linear Combination', '  Activation Output', '  Is Correct'])
+if not num_wrong:
+    print('Nice!  You got it all correct.\n')
+else:
+    print('You got {} wrong.  Keep trying!\n'.format(num_wrong))
+print(output_frame.to_string(index=False))