abishekk92

let mapleader=","

syntax enable

set nobackup
set nowritebackup

set background=dark

colorscheme desert

abishekk92

Keybase proof

I hereby claim:

• I am abishekk92 on github.
• I am abishekk92_sem3 (https://keybase.io/abishekk92_sem3) on keybase.
• I have a public key ASAKTPWub6-tnDxkc-prAFhEwHLv4_7jyQi3F4FD3pcoDwo

To claim this, I am signing this object:

abishekk92

# English to Japanese Dicitionary
jp_eng_str = "A= ka,  B= tu,  C= mi,   D= te,    E= ku,    F= lu,    G= ji,  H= ri,  I= ki,  J= zu,  K= me,   L= ta,  M= rin,   N= to,   O= mo,   P= no,   Q= ke,   R= shi,   S= ari,  T= chi,  U= do,   V= ru,   W= mei,   X= na,   Y= fu,   Z= zi" 
jp_eng_dict = dict(map(lambda a : a.strip().split("="), jp_eng_str.lower().split(",")))

#Convert an English word to a Japanese word.
to_jp = lambda word : "".join(map(lambda a : jp_eng_dict.get(a, '').strip(), word.lower())).title()

#Convert an English word to a Japanese name.
to_jp_name = lambda name : " ".join(map(to_jp, name.split(" ")))

abishekk92

So I tried training a 2 Layer GRU Encoder - Decoder Recurrent Neural Network to solve the well known fizzbuzz problem. 
For a max sequence length of 5 and 5K toy samples, the network was able to reach 98%  validation accuracy in 30 epochs.

Model Summary
============

____________________________________________________________________________________________________
Layer (type)                     Output Shape          Param #     Connected to
====================================================================================================
gru_1 (GRU)                      (None, 5, 128)        63744       gru_input_1[0][0]

abishekk92

Verifying that +abishekk92 is my blockchain ID. https://onename.com/abishekk92

abishekk92

Keybase proof

I hereby claim:

• I am abishekk92 on github.
• I am abishekk92 (https://keybase.io/abishekk92) on keybase.
• I have a public key whose fingerprint is 4D62 B23D C3B6 27C3 6223 AA80 270F A21D 45D6 8542

To claim this, I am signing this object:

abishekk92

final = Appendable()
for foo in bar:
  final.append(apply_map(apply_filter(foo)))
  

abishekk92

import util.Random.shuffle

object Sort{
    val toSort = List(1234, 23, 45, 56, 1)
    def insert(list : List[Int], t : Int) : List[Int] = list match {
        case Nil => List[Int](t)
        case x :: xs if x > t => t :: x :: xs
        case x :: xs => x :: insert(xs, t)
    }

abishekk92

I recently came across how one can parallelize an algorithm in terms of Map Reduce by nailing down the operations on the data as a well defined Algebraic Structure (A monoid). The idea has strong mathematical grounding and explains why Twitter is so much invested in Algebird, Summingbird. I will try to briefly explain what it is and why I am particularly fascinated by it.

Map Reduce

In today's world of ever growing data, the computation we need to run keeps getting complex and complex, these computations need to be run as efficiently(cost and resource) as possible. Most of these computations are run as Map Reduce jobs on a cluster of commodity grade hardware.

The idea behind Map Reduce is to move the computation to the data than to aggregate data to perform computation.

Each of the computation happen locally on the data(Mapping) on all the nodes at the same time, hence the notion of parallelism, these mapped data can be aggregated together and reduced into a representative value.

abishekk92

package abishekk92.executor

import abishekk92.Logging

import org.apache.mesos.{ Executor, ExecutorDriver }
import org.apache.mesos.Protos._

class Demo_frameworkExecutor extends Executor with Logging {