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// An example of using the PyTorch C++ API to implement a custom forward and backward function

#include <iostream>
#include <vector>

#include <torch/torch.h>
#include <torch/csrc/autograd/variable.h>
#include <torch/csrc/autograd/function.h>
#include <torch/csrc/autograd/VariableTypeUtils.h>
#include <torch/csrc/autograd/functions/utils.h>

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type below:

brew update
brew install redis

To have launchd start redis now and restart at login:

brew services start redis

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from selenium import webdriver
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.common.by import By
from selenium.webdriver.support import expected_conditions as EC
import requests
import os


def download_all_papers(base_url, save_dir, driver_path):
    driver = webdriver.Chrome(driver_path)

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# set a proxy
set HTTP_PROXY=
set HTTPS_PROXY=%HTTP_PROXY%
npm config set proxy %HTTP_PROXY%
npm config set https.proxy %HTTPS_PROXY%
npm config set https-proxy %HTTPS_PROXY%
git config --global http.proxy %HTTP_PROXY%
git config --global https.proxy %HTTPS_PROXY%

# unset proxy

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{-# LANGUAGE GADTs #-}
{-
  The following code is based on experimental code by Aslan Askerov
  based on Ramsey and Pfeffers "Stochastic Lambda Calculus and Monads of
  Probability Distributions". Implementation of random n is from
  Audebaud and Paulin-Mohring paper, so is the random walk example.

  This gist is used here http://madsbuch.com/blog/the-probability-monad/

  The class hierarchy is as follows:

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import matplotlib.pyplot as plt

def draw_neural_net(ax, left, right, bottom, top, layer_sizes):
    '''
    Draw a neural network cartoon using matplotilb.
    
    :usage:
        >>> fig = plt.figure(figsize=(12, 12))
        >>> draw_neural_net(fig.gca(), .1, .9, .1, .9, [4, 7, 2])
    

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loss=tf.reduce_mean(-elbo)
lr=tf.constant(0.001)
train_op=tf.train.AdamOptimizer(learning_rate=lr).minimize(loss,var_list=slim.get_model_variables())
init_op=tf.initialize_all_variables()
# get data
data = input_data.read_data_sets('/tmp/', one_hot=True).train 

BATCH_SIZE=100
NUM_ITERS=50000
tau0=1.0 # initial temperature

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Understanding LSTMs

• Japanese: qiita
• Russian: Alex Sosn
• Chinese: jianshu, csdn, csdn, csdn,

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package thunder.streaming

import org.apache.spark.{SparkConf, Logging}
import org.apache.spark.rdd.RDD
import org.apache.spark.SparkContext._
import org.apache.spark.streaming._
import org.apache.spark.streaming.dstream.DStream
import org.apache.spark.mllib.clustering.KMeansModel

import scala.util.Random.nextDouble

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from keras.layers import GRU, initializations, K
from collections import OrderedDict

class GRULN(GRU):
    '''Gated Recurrent Unit with Layer Normalization
    Current impelemtation only works with consume_less = 'gpu' which is already
    set.

    # Arguments
        output_dim: dimension of the internal projections and the final output.