In Python, which library is often used for deep learning tasks, including neural network construction and training, with a focus on simplicity and flexibility?

The correct answer is: B. Keras

Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It was developed with a focus on simplicity and flexibility, and provides a consistent API for building and training neural networks.

NumPy is a numerical Python library that provides fast, efficient numerical computation. It is the foundation of many scientific and engineering Python libraries, including Pandas, SciPy, and Matplotlib.

TensorFlow is an open-source software library for numerical computation using data flow graphs. It was originally developed by researchers and engineers from the Google Brain team within Google’s Machine Intelligence research organization for the purposes of machine learning and deep neural networks research, but the system is general enough to be applicable in a wide variety of other domains as well.

Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving a large number of variables. It is a popular choice for machine learning applications, as it provides a high-level API for building and training neural networks.

In conclusion, Keras is the most appropriate library for deep learning tasks in Python, as it is designed for simplicity and flexibility.