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#
# An example of calculating linear regression with gradient descent for a single variable.
# Kory Becker
# 1/31/2020
#

# Calculate linear regression with gradient descent.
linearRegression <- function(x, y, alpha, iterations) {
  # initialize coefficients
  theta <- matrix(c(0,0), nrow=2)

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How to submit a project to Udacity in Windows

When submitting a project to Udacity with a login on Google or Facebook, you will need to copy a jwt file to your PC in order for the login to work. Here's how.

1. Save the link https://project-assistant.udacity.com/auth_tokens/new to C:\Users\YOUR_USER_NAME\AppData\Roaming\udacity-pa
2. Open a CMD in Windows.
3. Navigate to your project source code folder:

   cd c:\projects\aind-sudoku

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from utils import *

# `grid` is defined in the test code scope as the following:
# (note: changing the value here will _not_ change the test code)
# grid = '..3.2.6..9..3.5..1..18.64....81.29..7.......8..67.82....26.95..8..2.3..9..5.1.3..'

def grid_values(grid):
    """Convert grid string into {<box>: <value>} dict with '.' value for empties.

    Args:

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library(data.tree)

entropy <- function(q) {
  # Calculate the entropy for a value.
  -1 * (q * log2(q) + (1 - q) * log2(1 - q))
}

positiveRatio <- function(data, outcomeCol = ncol(data)) {
  # Calculate the ratio of T by the total samples.
  positiveCount <- length(which(data[, outcomeCol] == T))

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entropy <- function(q) {
  # Calculate the entropy for a value.
  -1 * (q * log2(q) + (1 - q) * log2(1 - q))
}

positiveRatio <- function(data) {
  # Calculate the ratio of positives by the total measurements.
  sum(data$positives) / (sum(data$positives) + sum(data$negatives))
}

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library(ggplot2)

montyHall <- function(maxDoors = 3, switch = T, tries = 10) {
  plays <- data.frame()

    for (i in 1:tries) {
    # Create the doors.
    doors <- 1:maxDoors

    # Choose a winning door.

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Weak Supervision

Programmatically label millions of data points. See also here.

How does it work?

Ask domain experts for weak supervision signals (heuristics) to use as labeling functions, which can be programatically implemented for automatic labeling.

Examples of labeling functions: regular expressions, dependency trees, knowledge bases, crowdsourcing.

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Styles

https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/4.1.3/css/bootstrap.min.css
https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.11.2/css/all.min.css

Libraries

https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js
https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/4.3.1/js/bootstrap.min.js
https://cdnjs.cloudflare.com/ajax/libs/react/16.10.2/umd/react.production.min.js

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.cell {
  width: 50px;
  height: 50px;
  border: 1px solid black;
  transition: background-color 0.25s;
}

.player {
  font-size: 40px;
  position: absolute;

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$(function() {
  ReactDOM.render(
    <div>
      <App></App>
    </div>,
    document.getElementById('root')
  );
});

class Cell extends React.Component {