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Format: <type>(<scope>): <subject>
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<?php | |
/** | |
* Gravity Forms Bootstrap Styles | |
* | |
* Applies bootstrap classes to various common field types. | |
* Requires Bootstrap to be in use by the theme. | |
* | |
* Using this function allows use of Gravity Forms default CSS | |
* in conjuction with Bootstrap (benefit for fields types such as Address). | |
* |
def plot_learning_curves(estimator, X_train, y_train, X_val, y_val, | |
suptitle='', title='', xlabel='', ylabel=''): | |
""" | |
Plots learning curves for a given estimator. | |
Parameters | |
---------- | |
estimator : sklearn estimator | |
X_train : pd.DataFrame |
I have collected and moderated these ideas from various public sources and put into one place so that problem solvers and solution developers may find inspirations. Because I wish to update it regularly, I have setup as a single page wiki. You may try these ideas on hackathons/competitions/research; some are quite intense problems and some are not. Many of the problems were prepared keeping Dhaka/Bangladesh in mind, but of course can be applied to just about any underdeveloped/developing and sometimes developed countries.
Latency Comparison Numbers (~2012) | |
---------------------------------- | |
L1 cache reference 0.5 ns | |
Branch mispredict 5 ns | |
L2 cache reference 7 ns 14x L1 cache | |
Mutex lock/unlock 25 ns | |
Main memory reference 100 ns 20x L2 cache, 200x L1 cache | |
Compress 1K bytes with Zippy 3,000 ns 3 us | |
Send 1K bytes over 1 Gbps network 10,000 ns 10 us | |
Read 4K randomly from SSD* 150,000 ns 150 us ~1GB/sec SSD |
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