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Ali Aminian aliiaminian

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aliiaminian / energy.csv
Created April 10, 2018 13:16
State petrol
AL 163054
AK 1191884
AZ 67337
AR 87674
CA 1638019
CO 15552
CT 288349
DE 268773
DC 35499
@aliiaminian
aliiaminian / energy.csv
Created April 10, 2018 13:16
State Petroleum
AL 163054
AK 1191884
AZ 67337
AR 87674
CA 1638019
CO 15552
CT 288349
DE 268773
DC 35499
@aliiaminian
aliiaminian / gist:23bab1bf1192431d70fe4482ea88e2a8
Created April 10, 2018 13:10
State,EnergyGeneration
AL,163054
AK,1191884
AZ,67337
AR,87674
CA,1638019
CO,15552
CT,288349
DE,268773
DC,35499
@aliiaminian
aliiaminian / energygen.csv
Created April 10, 2018 12:59
State Petroleum
AL 163054
AK 1191884
AZ 67337
AR 87674
CA 1638019
CO 15552
CT 288349
DE 268773
DC 35499
@aliiaminian
aliiaminian / energygeneration.csv
Created April 10, 2018 12:26
State EnergyGeneration
AL 142960819.054
AK 6541674.545
AZ 112066600.784
AR 57499168.782
CA 204824248.739
CO 50451388.159
CT 31172259.581
DE 5010634.062
DC 35499
@aliiaminian
aliiaminian / insertion_sort.asm
Created September 14, 2017 21:42
An assembly code(mips) for sorting names using insertion sort
.globl main
.data
new_line : .asciiz "\n"
.align 5
space_char: .asciiz " "
.align 5
@aliiaminian
aliiaminian / mnist.py
Created September 5, 2017 00:44
Digit Recognition with tensorflow. MNIST dataset is used for both training and testing.
'''
mnist digit recognition with tf
by Ali Aminian
'''
import numpy as np
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
@aliiaminian
aliiaminian / minimal-sum-RNN.py
Last active September 4, 2017 15:55
This code creates a dataset of size 100000 of triples: two numbers of size max_length and their corresponding sum. Then, rnn learn to understand it. We sample frequently to see the progress. By calling test function we can see how well it works.
'''
Summation of two numbers using RNN model
by Ali Aminian
'''
import random
import sys
import os
import numpy as np
@aliiaminian
aliiaminian / minimal-char-RNN.py
Last active September 4, 2017 15:41
This minimal character level RNN can learn to predict next character, by having a large enough dataset. We sample a text of size 200 at some points to see the progress of its learning procedure. It is implemented simply with numpy and no deep learning packages used(such as tensorflow, caffe, ...) for the purpose of understanding the details.
'''
Minimal Character-level RNN model.
By Ali Aminian
'''
import numpy as np