Kaggle Wiki - some resources come from here.
ML:
Kaggle Wiki - some resources come from here.
ML:
"""Short and sweet LSTM implementation in Tensorflow. | |
Motivation: | |
When Tensorflow was released, adding RNNs was a bit of a hack - it required | |
building separate graphs for every number of timesteps and was a bit obscure | |
to use. Since then TF devs added things like `dynamic_rnn`, `scan` and `map_fn`. | |
Currently the APIs are decent, but all the tutorials that I am aware of are not | |
making the best use of the new APIs. | |
Advantages of this implementation: |
#!/usr/bin/env python3 | |
# vim: sta:et:sw=2:ts=2:sts=2 : | |
from copy import deepcopy as kopy | |
import sys,random | |
""" | |
Scott-Knot test + non parametric effect size + significance tests. | |
Tim Menzies, 2019. Share and enjoy. No warranty. Caveat Emptor. |