Working with TensorFlow Distributions for Probabilistic Modeling

Working with TensorFlow Distributions for Probabilistic Modeling

Unlock the potential of probabilistic modeling with TensorFlow Distributions. This powerful framework enhances statistical analysis by enabling efficient manipulation of various probability distributions, facilitating uncertainty representation, sampling, and inference for statisticians and machine learning experts alike.
Advanced Numerical Differentiation with scipy.misc.derivative

Advanced Numerical Differentiation with scipy.misc.derivative

Explore advanced numerical differentiation techniques using `scipy.misc.derivative` to efficiently approximate derivatives of functions. This guide covers forward, backward, and central difference methods, emphasizing accuracy, method selection, and practical applications in various computational fields, including physics and machine learning.