soumyasd

package transactions

import com.twitter.finagle.exp.mysql.{Client, OK, Result}

class Db(mysqlClient: Client) {

    val insertOrder     = "INSERT INTO orders (ref) VALUES(?)"
    val insertOrderItem = "INSERT INTO order_items (order_id, item_id) VALUES(?, ?)"

    def persistOrderWithItems[T](order: Order)(whenDone: (OK, Seq[Result]) => T): Future[T] = {

soumyasd

package lib

import fly.play.s3._
import java.io.OutputStream
import scala.concurrent.Future
import scala.concurrent.Await
import scala.concurrent.duration.Duration
import play.api.http.ContentTypeOf
import play.api.libs.concurrent.Execution.Implicits.defaultContext
import play.api.libs.concurrent.Akka 

soumyasd

Direct links to free Springer maths books (pdf versions)

duplicates = multiple editions

A Classical Introduction to Modern Number Theory,Kenneth IrelandMichael Rosen

A Classical Introduction to Modern Number Theory,Kenneth IrelandMichael Rosen

A Course in Arithmetic,Jean-Pierre Serre

soumyasd

import math
import random

def get_random_neighbour(state):
    neighbour = [house[:] for house in state] # Deep copy

    i = random.randint(0, 4)
    j = random.choice(range(0, i) + range(i+1, 4))

    attr_idx = random.randint(0, 4)

soumyasd

"""
Possibly correct implementation of an all conv neural network using a single residual module

This code was written for instruction purposes and no attempt to get the best results were made.

References:
Deep Residual Learning for Image Recognition: http://arxiv.org/pdf/1512.03385v1.pdf
STRIVING FOR SIMPLICITY, THE ALL CONVOLUTIONAL NET: http://arxiv.org/pdf/1412.6806v3.pdf

A video walking through the code and main ideas: https://youtu.be/-N_zlfKo4Ec

soumyasd

# Time Series Testing 
import keras.callbacks
from keras.models import Sequential  
from keras.layers.core import Dense, Activation, Dense, Dropout
from keras.layers.recurrent import LSTM

# Call back to capture losses 
class LossHistory(keras.callbacks.Callback):
    def on_train_begin(self, logs={}):
        self.losses = []