Deep Learning

  1. What will be the output of the following code? x = [ 1 , 2 , 3 ] y = x y.append( 4 ) print (x) A) [1, 2, 3] B) [1, 2, 3, 4] ✅ C) [4] D) Error Explanation: Lists are mutable, and y references the same object as x . So changes in y affect x . 2. Which of the following is not a valid keyword in Python? A) pass B) assert C) eval D) then ✅ Explanation: then is not a Python keyword. 3. What is the output? print ( bool ( 0 ), bool ( 3.5 ), bool (- 1 )) A) False True True ✅ B) False False False C) True True True D) Error Explanation: In Python, 0 is False. Any non-zero value (positive or negative) is True. 4. Which of these data types is immutable? A) list B) dict C) tuple ✅ D) set Explanation: Tuples are immutable. Lists, dicts, and sets are mutable. 5. What will the following code output? def f ( a, b= 2 , c= 3 ): return a + b + c print (f( 1 , c= 5 )) A) 6 B) 8 ✅ C) 10 D) Error Explanation: a=1, b=2 (default), c=5 (overridden) → ...

House Price Prediction   from tensorflow.keras.datasets import boston_housing from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense # Load dataset (x_train, y_train), (x_test, y_test) = boston_housing.load_data() # Normalize data mean, std = x_train.mean(axis= 0 ), x_train.std(axis= 0 ) x_train = (x_train - mean) / std x_test = (x_test - mean) / std # Define model model = Sequential([     Dense( 64 , activation= 'relu' , input_shape=(x_train.shape[ 1 ],)),     Dense( 64 , activation= 'relu' ),     Dense( 1 ) ]) # Compile model model. compile (optimizer= 'adam' , loss= 'mse' , metrics=[ 'mae' ]) # Train model model.fit(x_train, y_train, epochs= 50 , batch_size= 8 , validation_data=(x_test, y_test)) # -------- Prediction Step -------- # Predict on test set predictions = model.predict(x_test) # Display first 5 predictions with actual values for i in range ( 5 ):     print ( f "Predicted Price: {predict...

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