A neural network in Python is a type of artificial intelligence (AI) algorithm modeled after the structure and function of the human brain. It is used for tasks such as pattern recognition, image classification, and natural language processing. Neural networks consist of interconnected nodes, or artificial neurons, which process information and make decisions based on the input they receive.

To create a neural network in Python, one would typically use a deep learning library such as TensorFlow or PyTorch. These libraries provide pre-built neural network models and tools for training the network on data.

Here is a simple example of how to build a neural network using the popular deep learning library, Keras:

from keras.models import Sequential
from keras.layers import Dense

# Define the model
model = Sequential()
model.add(Dense(units=64, activation='relu', input_dim=100))
model.add(Dense(units=10, activation='softmax'))

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

# Train the model on data
model.fit(x_train, y_train, epochs=5, batch_size=32)

# Use the model to make predictions
predictions = model.predict(x_test)

In this example, we use the Sequential class from Keras to define a simple feedforward neural network with two dense layers. The first layer has 64 neurons and uses the ReLU activation function, while the second layer has 10 neurons and uses the softmax activation function. We then compile the model using a categorical crossentropy loss function and the stochastic gradient descent (SGD) optimizer. Finally, we train the model on some training data and use it to make predictions on new, unseen data.