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...

 https://drive.google.com/file/d/1l93Bv7MheGBbol6UmzsOz-2Du8OLj5mh/view?usp=sharing

  AIM :   Design Artificial Neural Networks for Identifying and Classifying an actor using Kaggle Dataset. Link to download Model file: https://drive.google.com/file/d/12lsyOHe5QifEaiW_d9K8BLLy5PeCEWsf/view?usp=sharing Link to Images to test: https://drive.google.com/drive/folders/1xhQJSzL_OL72YXBgdQD10_JmYpHPngLu?usp=sharing Program to predict Face Image's Age group: from tensorflow.keras.models import load_model from PIL import Image import numpy as np # Define the input image dimensions image_height = 128 image_width = 128 num_channels = 3 # Load the trained model model = load_model( '/content/drive/MyDrive/trained_model_C_Dataset.h5' ) # Replace 'your_trained_model.h5' with the actual file name # Read the new face image new_face_path = '/content/drive/MyDrive/2_Predict/old.jpeg'   # Replace 'path_to_new_face.jpg' with the actual file path new_face = Image. open (new_face_path) display(new_face) # Preprocess the image new_face = new_face.re...

  # Convolutional Neural Network # Installing Theano # pip install --upgrade --no-deps git+git://github.com/Theano/Theano.git # Installing Tensorflow # Install Tensorflow from the website: https://www.tensorflow.org/versions/r0.12/get_started/os_setup.html # Installing Keras # pip install --upgrade keras # Part 1 - Building the CNN # Importing the Keras libraries and packages from keras.models import Sequential from keras.layers import Convolution2D from keras.layers import MaxPooling2D from keras.layers import Flatten from keras.layers import Dense # Initialising the CNN classifier = Sequential() # Step 1 - Convolution classifier.add(Convolution2D( 32 , 3 , 3 , input_shape = ( 64 , 64 , 3 ), activation = 'relu' )) # Step 2 - Pooling classifier.add(MaxPooling2D(pool_size = ( 2 , 2 ))) # Adding a second convolutional layer classifier.add(Convolution2D( 32 , 3 , 3 , activation = 'relu' )) classifier.add(MaxPooling2D(pool_size = ( 2 , 2 ))) # Step 3 - Flattening ...