This quick guide for getting a Jupyter Notebook up and running on Bridges, a supercomputer managed by the Pittsburgh Supercomputing Center. Bridges is a new machine designed to accommodate non-traditional uses of High Performance Computing (HPC) resources like data science and digital humanities. Bridges is available through XSEDE, which is the system that manages access to multiple supercomputing resources. Through XSEDE, Bridges is available researchers or educators at US academic or non-profit research institutions (see the XSEDE eligibility policies) Allocations are free, but there is a somewhat difficult to understand application process filled with jargon and acronyms that take time to understand. See the XSEDE getting started guide for more information about getting acc
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# Helper function to plot a decision boundary. | |
# If you don't fully understand this function don't worry, it just generates the contour plot below. | |
def plot_decision_boundary(pred_func): | |
# Set min and max values and give it some padding | |
x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5 | |
y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5 | |
h = 0.01 | |
# Generate a grid of points with distance h between them | |
xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h)) | |
# Predict the function value for the whole gid |
I have moved this over to the Tech Interview Cheat Sheet Repo and has been expanded and even has code challenges you can run and practice against!
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