ianchen06

%%init_spark
# Configure the location of the mesos master and spark distribution on HDFS
# launcher.jars = ["/some/local/path/to/a/file.jar"]
launcher.packages = ["org.elasticsearch:elasticsearch-hadoop:6.6.0",
                     "org.apache.hadoop:hadoop-aws:2.7.5",
                     "org.apache.hadoop:hadoop-common:2.7.5",
                     "com.amazonaws:aws-java-sdk:1.7.4"]
launcher.master = "k8s://kubernetes.default"
launcher.conf.set("spark.submit.deployMode", "client")

jamestwebber

khuangaf

class NeuMF(torch.nn.Module):
    def __init__(self, config):
        super(NeuMF, self).__init__()

        #mf part
        self.embedding_user_mf = torch.nn.Embedding(num_embeddings=self.num_users, embedding_dim=self.latent_dim_mf)
        self.embedding_item_mf = torch.nn.Embedding(num_embeddings=self.num_items, embedding_dim=self.latent_dim_mf)
        
        #mlp part
        self.embedding_user_mlp = torch.nn.Embedding(num_embeddings=self.num_users, embedding_dim=self.latent_dim_mlp)

hereismari

Setting up a MSI laptop with GPU (gtx1060)

Installing Ubuntu 18.04, CUDA, CDNN, Pytorch and TensorFlow

Installing Ubuntu 18.04

Get Image

https://tutorials.ubuntu.com/tutorial/tutorial-create-a-usb-stick-on-ubuntu#0

Install (solving issues)

Orbifold

dirko

# Keras==1.0.6
from keras.models import Sequential
import numpy as np
from keras.layers.recurrent import LSTM
from keras.layers.core import TimeDistributedDense, Activation
from keras.preprocessing.sequence import pad_sequences
from keras.layers.embeddings import Embedding
from sklearn.cross_validation import train_test_split
from keras.layers import Merge
from keras.backend import tf

seanbehan

Let's Encrypt

• http://collectiveidea.com/blog/archives/2016/01/12/lets-encrypt-with-a-rails-app-on-heroku/
• https://certbot.eff.org/
• https://blog.heroku.com/archives/2016/5/18/announcing_heroku_free_ssl_beta_and_flexible_dyno_hours

monikkinom

#Source code with the blog post at http://monik.in/a-noobs-guide-to-implementing-rnn-lstm-using-tensorflow/
import numpy as np
import random
from random import shuffle
import tensorflow as tf

# from tensorflow.models.rnn import rnn_cell
# from tensorflow.models.rnn import rnn

NUM_EXAMPLES = 10000

softmonkeyjapan

class CategoriesController < ApplicationController
  include Behaveable::ResourceFinder
  include Behaveable::RouteExtractor

  # Response type.
  respond_to :json

  # Get categories.
  #
  # GET (/:categorizable/:categorizable_id)/categories(.:format)

karpathy

"""
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
BSD License
"""
import numpy as np

# data I/O
data = open('input.txt', 'r').read() # should be simple plain text file
chars = list(set(data))
data_size, vocab_size = len(data), len(chars)