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set nocompatible " be iMproved, required | |
filetype off " required | |
" set the runtime path to include Vundle and initialize | |
set rtp+=~/.vim/bundle/Vundle.vim | |
call vundle#begin() | |
" alternatively, pass a path where Vundle should install plugins | |
"call vundle#begin('~/some/path/here') | |
" let Vundle manage Vundle, required |
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# Split the data into training and test sets | |
X_train, X_test, y_train, y_test = train_test_split( | |
data_words, data_plant_types, test_size=0.33, random_state=42) | |
# Build a neural network | |
network = input_data(shape=[None, len(data_words[0])]) | |
network = fully_connected(network, 2048, activation='relu') | |
network = fully_connected( | |
network, len(data_plant_types[0]), activation='softmax') |
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"a4s_employee_full_name"=> | |
"\x04\bo:\eRepresentableDataValue\b:\v@fieldo:!EmployeeFullNameBuiltInField\x00:\f@recordo:\rEmployee\x10:\x10@attributeso:\x1FActiveRecord::AttributeSet\x06;\no:$ActiveRecord::LazyAttributeHash\n:\v@types}\x10I\"\aid\x06:\x06ETo:?ActiveRecord::ConnectionAdapters::PostgreSQL::OID::Integer\t:\x0F@precision0:\v@scale0:\v@limit0:\v@rangeo:\nRange\b:\texclT:\nbeginl-\a\x00\x00\x00\x80:\bendl+\a\x00\x00\x00\x80I\"\x0Fcreated_at\x06;\x0ETU:JActiveRecord::AttributeMethods::TimeZoneConversion::TimeZoneConverter[\t:\v__v2__[\x00[\x00o:@ActiveRecord::ConnectionAdapters::PostgreSQL::OID::DateTime\b;\x100;\x110;\x120I\"\x0Fupdated_at\x06;\x0ETU;\x18[\t;\x19[\x00[\x00@\x15I\"\fuser_id\x06;\x0ET@\fI\"\x1Aineligible_for_rehire\x06;\x0ETo: ActiveRecord::Type::Boolean\b;\x100;\x110;\x120I\"\euntracked_prior_tenure\x06;\x0ET@\fI\"\ttags\x06;\x0ETo:=ActiveRecord::ConnectionAdapters::PostgreSQL::OID::Array\a:\r@subtypeo:\x1DActiveRecord::Type::Text\b;\x100;\x110;\x120:\x0F@delimiterI\"\x06,\x06;\x0ET |
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# Git | |
# https://gist.github.com/n0n0x/7be453ff18f58020bafe | |
source ~/.git_bash_completion | |
# Go | |
export GOPATH=$HOME/development/go | |
export GOSRC=$GOPATH/src/github.com/LastZactionHero | |
export GOBIN=$GOPATH/bin | |
PATH=$PATH:$GOBIN | |
PATH=$PATH:/usr/local/go/bin |
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.NET | |
360-degree video | |
3D Animation | |
3D Design | |
3D Model Maker | |
3D Modelling | |
3D Printing | |
3D Rendering | |
3ds Max | |
4D |
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import tflearn | |
from tflearn.data_preprocessing import ImagePreprocessing | |
from tflearn.data_augmentation import ImageAugmentation | |
from tflearn.layers.core import input_data, dropout, fully_connected | |
from tflearn.layers.conv import conv_1d, max_pool_1d | |
from tflearn.layers.estimator import regression | |
img_prep = ImagePreprocessing() | |
img_prep.add_featurewise_zero_center() | |
img_prep.add_featurewise_stdnorm() |
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from sklearn import linear_model | |
Y = np.array(Y) # array of training answers, 1 or 0 | |
X = np.array([]) # array of image brightness | |
for filename in filenames: | |
# load the images | |
image = misc.imread(constants.IMAGE_64_PATH + '/' + filename, mode='L') | |
avg_brightness = np.matrix(image).mean() | |
X = np.append(X, avg_brightness) |
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import tflearn | |
from tflearn.data_preprocessing import ImagePreprocessing | |
from tflearn.data_augmentation import ImageAugmentation | |
from tflearn.layers.core import input_data, dropout, fully_connected | |
from tflearn.layers.conv import conv_1d, max_pool_1d | |
from tflearn.layers.estimator import regression | |
img_prep = ImagePreprocessing() | |
img_prep.add_featurewise_zero_center() |
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### THE SCRIPT | |
#!/usr/bin/env ruby | |
dump = `ps -ef | grep zeus` | |
processes = dump.split("\n") | |
processes.each do |process| | |
process_id = process.scan(/[0-9]+/)[1] | |
`sudo kill -9 #{process_id}` | |
end |
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x |