Deep Learning (DL) is a subset of machine learning that focuses on training deep neural networks with multiple layers. DL aims to model high-level abstractions in data by using multiple layers of nonlinear processing units. It has achieved remarkable success in various domains, including computer vision, natural language processing, and speech recognition.
Example code for a Deep Neural Network using the Keras library in Python:
import numpy as np
from keras.models import Sequential
from keras.layers import Dense
# Create a sequential model
model = Sequential()
# Add layers to the model
model.add(Dense(units=64, activation='relu', input_dim=100))
model.add(Dense(units=64, activation='relu'))
model.add(Dense(units=10, activation='softmax'))
# Compile the model
model.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])
# Generate dummy data
x_train = np.random.random((1000, 100))
y_train = np.random.randint(10, size=(1000, 1))
# Train the model
model.fit(x_train, y_train, epochs=10, batch_size=32)
# Make predictions
x_test = np.random.random((100, 100))
predictions = model.predict(x_test)
Note: The code example uses the Keras library, which provides a high-level interface for building and training neural networks.
