You can use NumPy's float16 data type in Python for memory efficiency in numerical computations. However, keep in mind that reduced precision can affect the outcome of complex mathematical operations [3]. To use float16, specify the data type when creating arrays:

import numpy as np
my_array = np.array([0.5, 0.5], dtype=np.float16)

For deep learning applications, using float16 for training is not recommended due to potential numeric stability issues [8]. Instead, consider using mixed precision, which is a mix of float16 and float32, by calling tf.keras.mixed_precision.experimental.set_policy('mixed_float16').

When working with float16, you might need to cast it back to float to obtain certain results [6]. This is because not all operations support float16 natively.

In summary, using float16 can be beneficial for memory-efficient numerical computations, but be aware of its limitations and potential impacts on accuracy and stability.