#Gnu octave electrical engineering series Octave software has extensive tools and rich libraries of content needed to solve linear algebra problems, find the roots of nonlinear equations, integrate ordinary functions, manipulate polynomials, integrate differential and differential-algebraic equations, and develop math projects in general. It is accommodated and can be easily expanded and configured by functions defined or user-written functions as well as modules written in C, C ++, Fortran and زبان languages. In addition to a separate component development environment, this programming language is available on VisualStudio and MinGW platforms. The name of the Octave software may at first glance indicate the program and the programming language as a music-related tool, but in fact the Octave is derived from the name of a professor of chemical reaction engineering.In the Stanford/Coursera machine learning class, Andrew Ng said that his teaching experience is that students pick up octave/matlab quicker and the course can cover more actual machine learning, compared to python where more time is spent learning the language. So I guess for newcomers and for quick prototyping (just 10s or 100s lines of code), octave is nicer. Say, you have some numbers for a matrix and a vector in files and want to read them in and multiple the vector by the matrix. In octave you are done in 3 lines of code in 30 seconds. In python, you first figure which modules to import, the difference between python arrays and numpy arrays, and god forbid you happen to find the numpy matrix type instead of numpy array type. After 5 minutes you think you're all set to calculate the M * v, but then your vector happens to be a line vector and not a column vector and you need to learn the difference. Also it's nicer to write M * v than np.dot(M,v). Most of the features of python that I presented as cons are actually pros when the codebase is more than 1000 lines and needs structure and safety. I took the EdX/MIT course on optimization methods and constraint solvers using Julia and JuMP and it was really fun. Julia wasn't around, or it was very early, when he designed the Machine Learning course or was teaching Stanford students, and he's moved on since then. I wonder if he would likely choose Julia now. #Gnu octave electrical engineering series.#Gnu octave electrical engineering code.#Gnu octave electrical engineering software.
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