Assignment -1

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

Comments

Post a Comment

Popular posts from this blog

11. List of Capstone Projects for SOC - Deep Learning

 List of Capstone Projects for SOC - Deep Learning  1. Build your Own Neural Net from Scratch. 2. Image Classification with CIFAR-10 Dataset.(Multi Class Classification) CNN 3. Human Face Detection.( detecting the bounding boxes of the human faces from a given Image)  YOLO  4. Traffic Sign Classification.(Collect Various traffic Signal Images and train Neural Network Model) CNN 5. Text Summarizer. RNN 6. Chatbot Using Deep Learning ( Training/Generative) 7. Colouring old B&W photos (Deep Neural Networks OpenCV) 8. Animal detection by training from multiple classes (CNN) 9. Face detection
Read more

8. Advanced CNN (Build AlexNet using Advanced CNN)

  Building and training an AlexNet-like model using the CIFAR-10 dataset in TensorFlow/Keras: Program to build the model: import tensorflow as tf from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout from tensorflow.keras.datasets import cifar10 from tensorflow.keras.utils import to_categorical from sklearn.model_selection import train_test_split # Load and preprocess CIFAR-10 dataset (x_train, y_train), (x_test, y_test) = cifar10.load_data() x_train = x_train.astype( 'float32' ) / 255.0 x_test = x_test.astype( 'float32' ) / 255.0 y_train = to_categorical(y_train, num_classes= 10 ) y_test = to_categorical(y_test, num_classes= 10 ) # Split into training and validation sets x_train, x_val, y_train, y_val = train_test_split(x_train, y_train, test_size= 0.1 , random_state= 42 ) # Define the model architecture '''model = tf.keras.Sequential([     Conv2D(96, (11, 11), strides=(4, 4), activation='relu', input_shape=(32...
Read more