I'm learning about probability density functions (pdf) and cumulative distribution functions (cdf) this week in class. I didn't realize how applicable statistics is to programming.
It's really essential in for example state-of-the-art machine learning. For example, the commonly used SoftMax function is used to turn an arbitrary vector into a pd.
It was discovered that plt.errorbar() does not tolerate any native yerr_data! Thus, your code needed to be modified as seen here: yerr_data = np.abs(0.1*np.random.randn(len(x_data))) YERROR MUST ALWAYS BE POSITIVE TO AVOID TRACEBACK ERRORS!
Nice 1. what if using the KDE instead the χ^2 to find the pdf of a set of data, sometimes distributed differently and with some "outliers"? 2. Why not to use the sklearn to fit quite automatically (without specifying the curve fit equation ) a set of data?
wait..... you mean you're not a bird?
Lmfao
Nah, Billy just coded up a next level ML model that creates the human avatar you see
Birds are not real bro
I'm learning about probability density functions (pdf) and cumulative distribution functions (cdf) this week in class. I didn't realize how applicable statistics is to programming.
It's really essential in for example state-of-the-art machine learning. For example, the commonly used SoftMax function is used to turn an arbitrary vector into a pd.
Without any doubt, the best videos on scientific applications of Python. Thank you very much.
Great explanation. Also love the basic ones such as the class video from zero. Please do more of those!
Great explanation! Just in time before a lab session where I’ll have to do nonlinear fitting
It was discovered that plt.errorbar() does not tolerate any native yerr_data!
Thus, your code needed to be modified as seen here:
yerr_data = np.abs(0.1*np.random.randn(len(x_data)))
YERROR MUST ALWAYS BE POSITIVE TO AVOID TRACEBACK ERRORS!
You should do a tutorial on Lmfit. It's really advanced and built on top curve fit.
Thank you! Your tutorials are very clear and useful!
Great content! Hope that you'll continue making more of these videos :)
Nice
1. what if using the KDE instead the χ^2 to find the pdf of a set of data, sometimes distributed differently and with some "outliers"?
2. Why not to use the sklearn to fit quite automatically (without specifying the curve fit equation ) a set of data?
at 11:01, are the parameters in the formula for a normal distribution flipped?
Ah yes, good catch!
Awesome, simply a bull's eye explanation
It raised an error even for error bar graph saying: ValueError: 'yerr' must not contain negative values
What if I don't have an error for each point?
Can you please do a video on linear programming in python with PULP library !!!
Wait why was i expecting a bird to appear?
I Imagine Billy trying to do this. Poor Billy. Not that I would do much better before watching the video 😅
We don't want u. We want billy.
Wait guys ... where is the Birdie ?
You the best!
Where billy
hi billy
😭 *PromoSM*