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MartinThoma / xception_summary.txt
Created Feb 11, 2019
Keras model summary of Xception (Image classification deep learning model)
View xception_summary.txt
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) (None, None, None, 3 0
__________________________________________________________________________________________________
block1_conv1 (Conv2D) (None, None, None, 3 864 input_1[0][0]
__________________________________________________________________________________________________
block1_conv1_bn (BatchNormaliza (None, None, None, 3 128 block1_conv1[0][0]
__________________________________________________________________________________________________
block1_conv1_act (Activation) (None, None, None, 3 0 block1_conv1_bn[0][0]
View amentment.md

Code

UTC time with offset:

>>> import pytz
>>> tz = pytz.timezone('Pacific/Apia')
>>> import datetime
>>> datetime.datetime(2011, 12, 30, 9, 59, tzinfo=datetime.timezone.utc).astimezone(tz).isoformat()
'2011-12-29T23:59:00-10:00'
>>> datetime.datetime(2011, 12, 30, 10, 00, tzinfo=datetime.timezone.utc).astimezone(tz).isoformat()
View gist:85080a53f73661f971a7ef4dba48364b
Decision trees might even be in $\mathcal{O}(1)$. I have to give impurity measures a closer thought.
# Time Complexity of Training and Testing
## By training samples ($n$) and samples features ($m$) and number of classes ($c$).
The following classifiers potentially have computational complexities less than or equal with $\mathcal{O}(mnc)$ complexity) in both of the training and testing phases.
* k-nearest neighbors is linear (https://stats.stackexchange.com/questions/219655/k-nn-computational-complexity),
* Naive Bayes is linear for those PDFs that can be estimated in linear time (e.g. Poisson and Multinomial PDFs).
* Approximate SVM is linear (https://stats.stackexchange.com/questions/96995/machine-learning-classifiers-big-o-or-complexity)
View data-science-wordcloud.txt
11 Python #509e2f
10 Computer~Science
10 Mathematics
9 R #509e2f
8 machine~learning #651d32
7 Big~Data #41b6e6
6 Java #509e2f
5 Databases
5 Hadoop #41b6e6
4 Tensorflow/Keras/Theano #651d32
@MartinThoma
MartinThoma / diff.txt
Created May 28, 2018
Difference between mtrand.cpython-36m-x86_64-linux-gnu.so of numpy 1.14.0 vs numpy 1.14.1
View diff.txt
_rand_bool(low, high, size, rngstate)
Return random np.bool_ integers between ``low`` and ``high``, inclusive.
Return random integers from the "discrete uniform" distribution in the
closed interval [``low``, ``high``). On entry the arguments are presumed
to have been validated for size and order for the np.bool_ type.
Parameters
----------
low : int
Lowest (signed) integer to be drawn from the distribution.
high : int
View linear_regression.py
#!/usr/bin/env python
import numpy as np
from sklearn.linear_model import LinearRegression
import sklearn.metrics
regressor = LinearRegression()
n = 4
feature_dim = 2
View gist:fa4deb2b4c71ffcd726b24b7ab581ae2
Python 3.5.2 (default, Nov 23 2017, 16:37:01)
[GCC 5.4.0 20160609] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import daemon
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/local/lib/python3.5/dist-packages/daemon.py", line 70
os.umask(022) # Don't allow others to write
^
SyntaxError: invalid token
View pandas_example.py
#!/usr/bin/env python
"""What is the difference between map and apply?"""
import pandas as pd
df = pd.DataFrame([(1, 2, 3), (5, 6, 7), (9, 0, 1), (3, 4, 5)],
columns=list('abc'),
index=['India', 'France', 'England', 'Germany'])
View gist:e9b072c5314d3d5d2507aba8a86b0d6e
>>> df = pd.DataFrame([{'id': 1, 'price': 123, 'name': 'anna', 'amount': 1},
... {'id': 1, 'price': 7, 'name': 'anna', 'amount': 2},
... {'id': 2, 'price': 42, 'name': 'bob', 'amount': 30},
... {'id': 3, 'price': 1, 'name': 'charlie', 'amount': 10},
... {'id': 3, 'price': 2, 'name': 'david', 'amount': 100}])
>>> df
amount id name price
0 1 1 anna 123
1 2 1 anna 7
View .config
[core]
repositoryformatversion = 0
filemode = true
bare = false
logallrefupdates = true
[remote "origin"]
url = git@github.com:MartinThoma/scipy.git
fetch = +refs/heads/*:refs/remotes/origin/*
[branch "master"]
remote = origin
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