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Search - "python matplotlib"
Python Tools to Get Started with Machine Learning
SciPy - the most fundamental library with essential packages such as NumPy, matplotlib, Pandas, and SymPy.
NumPy - gives you the ability to play with your data as 'arrays' using some powerful array functions and linear algebra functions. Very essential since most computing is done with arrays of numbers.
Matplotlib - to visualize data and model outputs using 2D plotting with some 3D functionality.
Pandas - a highly flexible package which introduces dataframes to Python, a type of in-memory data table. Makes it easy to understand the data's structure and provides easy to use SQL-like commands to play with the data.
SymPy - is a package used for symbolic mathematics and computer algebra.
StatsModels - commonly used package for statistical methods and algorithms.
Scikit-learn - Most popular and easy to understand library filled with machine learning algorithms. A good start for beginners and practitioners working with smaller data loads.
RPy2 - A cross between Python and R. Allows you to call R functions from within Python.
NLTK (Natural Language Toolkit) - this toolkit in Python has functions and methods for text analysis.13
My boi states that node's biggest issue has always been npm and the quality of packages. I always contradict those statements by saying that if one uses community standards and the best packages then one does not need to worry about the quality(i.e mongoose over some unmaintained mongo wrapper etc)
I sometimes catch myself finding that my way of thinking adapts better to JS than it even does Python (which is his preference for deep learning) and whilst there are some beastly packages for python in terms of quality and usefulness such as matplotlib etc that one can do great things with the equivalent JS.
I mean, tensorflow.js came from the same wizards that did tensorflow (obviously) and i find the functional approach of JS to be more on par with how we develop solutions.
I am no deep learning expert, and sadly I have no professional experience with machine learning. But I venture to say that we should not cast aside the great strides that the JS community has done to the language in terms of evolution and tooling. Today's Js is not your grandaddy's Js and thinking that the language is crippled because of early iterations of the language would be severely biased.
What do you guys(maybe someone with professional experience) think of Js as a language for machine learning?
Do you think the language poses something worth considering in terms of tooling and power for ml?3
The documentation for the matplotlib python library is terrible for newbies.
There is a "Tutorial" section, but the thing doesn't even explain what you can do until you get to the 4th section!
It starts off with some confusing examples, how to change the appearance and only at section 4 do you actually start to get an introduction to the different components you might want to use...
At some point you finally realize, most of the stuff that is shown can be omitted because the .pyplot module is all you need.
i was learning neural networks, started with keras and was on the first tutorial where they started by importing pandas
so i switched to learning data analysis using pandas in Python where they started by importing matplotlib and i realized data visualization is also important and now I'm reading matplotlib docs...🙄11
So matplotlib can do 3d plots. However, when you try to then label your axes...
plt.xlabel("protocol") # ok
plt.ylabel("volume") # ok
plt.zlabel("time") # error: no such method zlabel (ಠ_ಠ)2
I am using python and matplotlib as a substitute for microsoftword plotting .. could i count myself as a programmer ? Lol