Mark Markaryan markmark206
markmark206
/ US Zip Codes from 2013 Government Data
Created
September 26, 2017 17:06
— forked from erichurst/US Zip Codes from 2013 Government Data
All US zip codes with their corresponding latitude and longitude coordinates. Comma delimited for your database goodness. Source: http://www.census.gov/geo/maps-data/data/gazetteer.html
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| ZIP,LAT,LNG | |
| 00601,18.180555, -66.749961 | |
| 00602,18.361945, -67.175597 | |
| 00603,18.455183, -67.119887 | |
| 00606,18.158345, -66.932911 | |
| 00610,18.295366, -67.125135 | |
| 00612,18.402253, -66.711397 | |
| 00616,18.420412, -66.671979 | |
| 00617,18.445147, -66.559696 | |
| 00622,17.991245, -67.153993 |
markmark206
/ simple_mlp_tensorflow.py
Created
April 24, 2017 04:28
— forked from vinhkhuc/simple_mlp_tensorflow.py
Simple Feedforward Neural Network using TensorFlow
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| # Implementation of a simple MLP network with one hidden layer. Tested on the iris data set. | |
| # Requires: numpy, sklearn>=0.18.1, tensorflow>=1.0 | |
| # NOTE: In order to make the code simple, we rewrite x * W_1 + b_1 = x' * W_1' | |
| # where x' = [x | 1] and W_1' is the matrix W_1 appended with a new row with elements b_1's. | |
| # Similarly, for h * W_2 + b_2 | |
| import tensorflow as tf | |
| import numpy as np | |
| from sklearn import datasets | |
| from sklearn.model_selection import train_test_split |
markmark206
/ simple_mlp_tensorflow.py
Created
April 24, 2017 04:28
— forked from vinhkhuc/simple_mlp_tensorflow.py
Simple Feedforward Neural Network using TensorFlow
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| # Implementation of a simple MLP network with one hidden layer. Tested on the iris data set. | |
| # Requires: numpy, sklearn>=0.18.1, tensorflow>=1.0 | |
| # NOTE: In order to make the code simple, we rewrite x * W_1 + b_1 = x' * W_1' | |
| # where x' = [x | 1] and W_1' is the matrix W_1 appended with a new row with elements b_1's. | |
| # Similarly, for h * W_2 + b_2 | |
| import tensorflow as tf | |
| import numpy as np | |
| from sklearn import datasets | |
| from sklearn.model_selection import train_test_split |