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Changing the world :)

Favio André Vázquez FavioVazquez

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Changing the world :)
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install.packages("remotes")
remotes::install_github("JohnCoene/g2r")
# So normally this is what you do for getting a plot with ggplot2
library(ggplot2)
ggplot(iris, aes(Petal.Length, Petal.Width, color = Species)) +
geom_point() +
facet_wrap(.~Species)
We can make this file beautiful and searchable if this error is corrected: It looks like row 4 should actually have 23 columns, instead of 3. in line 3.
id,conversation_id,created_at,date,time,timezone,user_id,username,name,place,tweet,mentions,urls,photos,replies_count,retweets_count,likes_count,location,hashtags,link,retweet,quote_url,video
1124775205698719744,1124775205698719744,1557002281000,2019-05-04,15:38:01,CDT,788898706586275840,tdatascience,Towards Data Science,,Optimal Control: LQR by @vlastelicap https://buff.ly/2ZT45ud ,['vlastelicap'],['https://buff.ly/2ZT45ud'],[],0,0,1,,[],https://twitter.com/TDataScience/status/1124775205698719744,,,0
1124733938625384448,1124733938625384448,1556992442000,2019-05-04,12:54:02,CDT,788898706586275840,tdatascience,Towards Data Science,,Who owns your health data? https://buff.ly/2Ywezz0 🖊by @jaynew_l #healthcare #BigData #TDSPick 🎲 pic.twitter.com/C2BbVyvyky,['jaynew_l'],['https://buff.ly/2Ywezz0'],['https://pbs.twimg.com/media/D5vbjoVW4AA0FzV.jpg'],0,1,0,,"['#healthcare', '#bigdata', '#tdspick']",https://twitter.com/TDataScience/status/1124733938625384448,,,0
1124697946887532544,1124697946887532544,1556983861000
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import tensorflow as tf
__weights_dict = dict()
is_train = False
def load_weights(weight_file):
import numpy as np
if weight_file == None:
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
__weights_dict = dict()
def load_weights(weight_file):
if weight_file == None:
return
'''Train a simple deep CNN on the CIFAR10 small images dataset.
GPU run command:
THEANO_FLAGS=mode=FAST_RUN,device=gpu,floatX=float32 python cifar10_cnn.py
It gets down to 0.65 test logloss in 25 epochs, and down to 0.55 after 50 epochs.
(it's still underfitting at that point, though).
Note: the data was pickled with Python 2, and some encoding issues might prevent you
from loading it in Python 3. You might have to load it in Python 2,
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