Building a model using TensorFlow.

To build a model using TensorFlow, you first need to install the TensorFlow library. Then, you can follow these steps to construct the model:

1. Import the necessary libraries and modules:

import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense

1. Create a Sequential model.

model = Sequential()

1. Add layers to the model.

model.add(Dense(units=64, activation='relu', input_shape=(10,)))
model.add(Dense(units=64, activation='relu'))
model.add(Dense(units=1, activation='sigmoid'))

1. Compile model:

model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

1. training model

model.fit(X_train, y_train, epochs=10, batch_size=32)

X_train represents the features of the training data, while y_train represents the labels of the training data.

1. Evaluate the model.

loss, accuracy = model.evaluate(X_test, y_test)
print('Test accuracy:', accuracy)

By following these steps, you can build and train a deep learning model using TensorFlow.