quick-references
Ipython Overview
In the realm of data science and computational research, having a powerful and flexible interactive computing environment is crucial for efficient analysis, experimentation, and exploration. IPython, an enhanced interactive Python shell, stands as a highly versatile and feature-rich tool that has revolutionized the way researchers, scientists, and programmers work with Python.
Overview on Numpy
In the realm of scientific computing and data analysis, NumPy (Numerical Python) stands as one of the most essential libraries. Built on top of Python, NumPy provides a powerful framework for efficient manipulation and computation with large multi-dimensional arrays and matrices.
Ipython
Python has gained immense popularity among developers and data scientists due to its simplicity and flexibility. One of the key reasons for Python's success is its extensive ecosystem of libraries that enable users to tackle a wide range of tasks efficiently. Among these libraries, IPython stands out as a powerful tool for interactive computing and data exploration.
Artificial Intelligence Overview
In the age of rapidly advancing technology, few innovations have captured the imagination and potential for transformation quite like Artificial Intelligence (AI). Whether it's powering voice assistants on our smartphones, driving autonomous vehicles, or revolutionizing industries such as healthcare and finance, AI has become an integral part of our daily lives.
Go Pointer Receiver Nuances
A pointer receiver in Go is a method receiver that is defined with a pointer type. It allows a method to modify the value of the receiver itself or access the underlying data the receiver points to. When a method has a pointer receiver, the changes made to the receiver inside the method persist outside the method.
Posted June 12, 2023 by Rohith ‐ 4 min read