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?
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.
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
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
Great content! Hope that you'll continue making more of these videos :)
Thank you! Your tutorials are very clear and useful!
You should do a tutorial on Lmfit. It's really advanced and built on top curve fit.
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?
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 !!!
at 11:01, are the parameters in the formula for a normal distribution flipped?
Ah yes, good catch!
You the best!
I Imagine Billy trying to do this. Poor Billy. Not that I would do much better before watching the video 😅
It should be N_i(0,1), because you defined N_i(µ, sigma) and always said that sigma should equal 1
otherwise, great video of course
We don't want u. We want billy.
Wait why was i expecting a bird to appear?
Wait guys ... where is the Birdie ?
Where billy
hi billy
😭 *PromoSM*