osdc-2023-01-public VS NumPy

Welcome back dear reader. Let me remind you that I am Silent-Mobius, also known as Alex M. Schapelle, your faithful narrator. As you might remember, I have applied to short journey of sort, whose main orchestrator is @szabgab, under a code name of #OSCD. Despite the fact the journey is still under its first steps, I've been willing to remember it with any possible tool I can have under my utility belt. That is why I've decided, to use #git. Git is a free and open source distributed version control system designed to handle everything from small to very large projects with speed and efficiency.

As part of the fourth assignment of the OSDC we had to practice GitHub and create a new project.

I am taking Open Source Development Course given by Gabor Szabo in order to gain a better understanding of this field. In the second session we learned about github pages which is an easy way for building a website. Anyone can do it, even if they don't have programming experience. All you need is a github account!

Through a shared colleague, I recently got to know Gabor Szabo. Luckily, he invited me to join his Open Source Development Course.

And he also created a practice website which we can update

I have done my second assignment for the Open Source Development course (OSDC). The assignment was to practice markdowns in GitHub.

In NumPy with @, dot() or matmul():

In NumPy with * or multiply(). ` or multiply()` can multiply 0D or more D arrays by element-wise multiplication.

Data science projects often use numpy. However, numpy objects are not JSON-serializable and therefore require conversion to standard python objects in order to be saved:

numpy: A library for scientific computing in Python

Python has become a preferred language for data analysis due to its simplicity and robust library ecosystem. Among these, NumPy stands out with its efficient handling of numerical data. Let’s say you’re working with numbers for large data sets—something Python’s native data structures may find challenging. That’s where NumPy arrays come into play, making numerical computations seamless and speedy.

I was looking at all the big projects like numpy, pytorch, flask, etc.

I wish the NumPy devs would more thoroughly consider adding full fluent API support, e.g. x.sqrt().ceil(). [Issue #24081]