Jalem Raj Rohit Dawny33

I hereby claim:

I am Dawny33 on github.
I am dawny33 (https://keybase.io/dawny33) on keybase.
I have a public key whose fingerprint is DD01 24DB 0983 7AAC C6FE 1427 0A5B F308 6A57 CBBA

To claim this, I am signing this object:

A list of bite-sized projects:

A to-do app
- Strike-off when done (Not delete)
Automated ML pipeline with Luigi/Airflow
A fully-automatic client-server framework (using deep neural nets) to identify lost domestic animals based only on images/videos uploaded from the owner and a short video uploaded from the finder!
Create a service which takes string data and returns capitalized version of that data
Convert Natural Language to SQL queries

Title: Introduction to Microeconomics

Microeconomics is all about scarcity. It is about how individuals and companies make decisions, given that we live in a world of scarcity.
Two actors in the economy: Consumers and Producers
Consumers have limited(are constrained by) wealth. So, they maximize utility subject to a budget constraint.
Producers maximize profits(pi) subject to consumer demand and input costs.
3 fundamental questions of microeconomics:
- What goods and services should be produced?
- How to produce them?
Who gets them?

	This gist is a simple explanation about how to connect and access your Jupyter notebooks on the P2 instance, from your local browser.

	I had some problems with directly accessing port 8888 of my server for Jupyter. However, the following steps helped in sorting that out.

	The following steps assume that you already have a P2 instance running with the `DeepLearning Ubuntu` AMI

	- Jupyter would already be installed in your server if you are running the DeepLearning AMI

	- Start Jupyter with `jupyter notebook`

	# Implementation of a simple MLP network with one hidden layer. Tested on the iris data set.
	# Requires: numpy, sklearn>=0.18.1, tensorflow>=1.0

	# NOTE: In order to make the code simple, we rewrite x * W_1 + b_1 = x' * W_1'
	# where x' = [x \| 1] and W_1' is the matrix W_1 appended with a new row with elements b_1's.
	# Similarly, for h * W_2 + b_2
	import tensorflow as tf
	import numpy as np
	from sklearn import datasets
	from sklearn.model_selection import train_test_split

	import paramiko

	k = paramiko.RSAKey.from_private_key_file("YOUR_PEM_FILE.pem")
	c = paramiko.SSHClient()
	c.set_missing_host_key_policy(paramiko.AutoAddPolicy())
	c.connect( hostname = "ec2-1-1-1-1.us-west-2.compute.amazonaws.com", username = "ec2-user", pkey = k )

	stdin , stdout, stderr = c.exec_command("hostname")
	print("stdout: " + stdout.read())
	print("stderr" + stderr.read())

	#-- coding:utf-8 - -


	def load_dataset():
	"Load the sample dataset."
	return [[1, 3, 4], [2, 3, 5], [1, 2, 3, 5], [2, 5]]


	def createC1(dataset):
	"Create a list of candidate item sets of size one."