2. Speech Recognition: Deep learning is used in speech recognition systems such as Google Assistant and Siri, allowing these systems to understand and respond to human speech. 3. Machine Translation: Services like Google Translate use deep learning to improve the accuracy of translating texts between different languages. #### Common Deep Learning Models: 1. Convolutional Neural Networks (CNNs): Mainly used for image processing tasks and object recognition. 2. Recurrent Neural Networks (RNNs): Used to process sequential data, such as texts or time series. For example, RNNs are used in machine translation and speech recognition. 3. Transformers: These are widely used in natural language processing tasks such as machine translation and text understanding. #### Code Example (Using Keras and TensorFlow) Here is a simple example of building a model using Convolutional Neural Networks (CNN) for image recognition:

import tensorflow as tf
from tensorflow.keras import layers, models

# Load the MNIST dataset (images of digits)
mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()

# Normalize the data
x_train, x_test = x_train / 255.0, x_test / 255.0

# Build the model using CNN
model = models.Sequential([
    layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)),
    layers.MaxPooling2D((2, 2)),
    layers.Conv2D(64, (3, 3), activation='relu'),
    layers.MaxPooling2D((2, 2)),
    layers.Flatten(),
    layers.Dense(64, activation='relu'),
    layers.Dense(10, activation='softmax')
])

# Compile the model
model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

# Train the model
model.fit(x_train, y_train, epochs=5)

# Test the model
test_loss, test_acc = model.evaluate(x_test, y_test)
print(f"Model accuracy on test set: {test_acc}")

These examples should give beginners a clear idea of what deep learning is, along with practical examples of how to use it. :)