6. Demystifying PI in Python: Exploring math.pi, numpy.pi, and scipy.pi
   What they are:scipy. pi, numpy. pi, and math. pi are all ways to access the mathematical constant pi (π) in Python. They provide the value of pi
7. Approximating Derivatives using Python Libraries
   Numerical Differentiation with numpy. gradientThe most common approach in NumPy is to use the numpy. gradient function for numerical differentiation
8. Identifying Not a Number (NaN) in Python: The math.isnan() Method
   What is NaN?In floating-point arithmetic (used for decimal numbers), NaN represents a result that's not a valid number.It can arise from operations like dividing by zero
9. Adaptive Average Pooling in Python: Mastering Dimensionality Reduction in Neural Networks
   Adaptive Average PoolingIn convolutional neural networks (CNNs), pooling layers are used to reduce the dimensionality of feature maps while capturing important spatial information
10. Choosing the Right Division Operator in Python: '/' (True Division) vs. '//' (Floor Division)
    Understanding Division in Python:Python offers two distinct division operators, each with its specific function:'/' (Forward Slash): This operator performs true division
11. Demystifying numpy.max, numpy.amax, and maximum: Finding Maximum Values in Python
    numpy. max and numpy. amax:These functions are essentially the same and behave identically. They both calculate the maximum value within an array