jake-westfall

library("scatterplot3d")
library("MASS")

path <- "/Users/jakewestfall/Desktop/"

# simulate data from gaussian copula
covmat <- matrix(.9, nrow=3, ncol=3)
diag(covmat) <- 1
dat <- pnorm(mvrnorm(n=3000, mu=c(0,0,0), Sigma=covmat))
# pairs(dat)

dsquintana

### Contour-enhanced funnel plots using metafor ###

# Load metafor package

library("metafor")

# Load dataset

dat <- get(data(dat.molloy2014)) 

b03902043

from keras import backend as K
from keras.layers import TimeDistributed, Dense, LSTM

class AttentionLSTM(LSTM):
    """LSTM with attention mechanism
    This is an LSTM incorporating an attention mechanism into its hidden states.
    Currently, the context vector calculated from the attended vector is fed
    into the model's internal states, closely following the model by Xu et al.
    (2016, Sec. 3.1.2), using a soft attention model following
    Bahdanau et al. (2014).

Pakillo

https://github.com/brentthorne/posterdown

https://github.com/rstudio/pagedown

https://github.com/GerkeLab/betterposter

https://github.com/pzhaonet/postr

https://github.com/odeleongt/postr

alexeygrigorev

import requests
import base64
from tqdm import tqdm


master_json_url = 'https://178skyfiregce-a.akamaihd.net/exp=1474107106~acl=%2F142089577%2F%2A~hmac=0d9becc441fc5385462d53bf59cf019c0184690862f49b414e9a2f1c5bafbe0d/142089577/video/426274424,426274425,426274423,426274422/master.json?base64_init=1'
base_url = master_json_url[:master_json_url.rfind('/', 0, -26) + 1]

resp = requests.get(master_json_url)
content = resp.json()