Dawny33

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?

Dawny33

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

Dawny33

#-*- 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."

Dawny33

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())

Dawny33

# 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

Dawny33

Keybase proof

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:

Dawny33

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`