granturing

<!-- place this in an %angular paragraph -->

<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/leaflet/0.7.5/leaflet.css" />
<div id="map" style="height: 800px; width: 100%"></div>

<script type="text/javascript">
function initMap() {
    var map = L.map('map').setView([30.00, -30.00], 3);

    L.tileLayer('http://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png', {

alexey-milovidov

:) CREATE TABLE test.nested (EventDate Date, UserID UInt64, Attrs Nested(Key String, Value String)) ENGINE = MergeTree(EventDate, UserID, 8192)

CREATE TABLE test.nested
(
    EventDate Date, 
    UserID UInt64, 
    Attrs Nested(
    Key String, 
    Value String)
) ENGINE = MergeTree(EventDate, UserID, 8192)

Kaixhin

--[[
--   Element-Research Torch RNN Tutorial for recurrent neural nets : let's predict time series with a laptop GPU
--   https://christopher5106.github.io/deep/learning/2016/07/14/element-research-torch-rnn-tutorial.html
--]]

--[[
--   Part 1
--]]

require 'rnn'

philipperemy

from __future__ import print_function

import numpy as np
from keras.callbacks import Callback
from keras.layers import Dense
from keras.layers import LSTM
from keras.models import Sequential
from numpy.random import choice

from utils import prepare_sequences

georgeblck

###
### This is a batched LSTM forward and backward pass. Written by Andrej Karpathy (@karpathy)
### BSD License
### Re-written in R by @georgeblck
###

rm(list=ls(all=TRUE))

LSTM.init <- function(input_size, hidden_size, fancy_forget_bias_init = 3){
  # Initialize parameters of the LSTM (both weights and biases in one matrix) 

DMTSource

# Derek M. Tishler
# nx plot example deap individual
# For post: https://groups.google.com/g/deap-users/c/nZFZpm5OPZA

import matplotlib.pyplot as plt
import networkx as nx
from networkx.drawing.nx_agraph import graphviz_layout

## May need is resuming session
#from deap import creator