The Theorem Every Data Scientist Should Know (Part 2) · Jean-Nicholas Hould

The Theorem Every Data Scientist Should Know (Part 2)  #MachineLearning #DataScience

  • The standard deviation of the population distribution is tied with the standard deviation of the sampling distribution.
  • With the standard deviation of the sampling distribution and the sample size, we are able to calculate the standard deviation of the population distribution.
  • The standard deviation of the sampling distribution is called the standard error.
  • The mean of the sampling distribution will cluster around the population mean.
  • If we collect a large number of different samples mean, the distribution of those samples mean should take the shape of a normal distribution no matter what the population distribution is.

Last week, I wrote a post about the Central Limit Theorem. In that post, I explained through examples what the theorem is and why it’s so important when working with data. If you haven’t read it yet, go do it now. To keep the post short and focused, I didn’t go into many details. The goal of that post was to communicate the general concept of the theorem. In the days following it’s publication, I received many messages. People wanted me to go into more details.
Continue reading “The Theorem Every Data Scientist Should Know (Part 2) · Jean-Nicholas Hould”

The Theorem Every Data Scientist Should Know (Part 2) · Jean-Nicholas Hould

The Theorem Every Data Scientist Should Know (Part 2)  #MachineLearning #DataScience

  • The standard deviation of the population distribution is tied with the standard deviation of the sampling distribution.
  • With the standard deviation of the sampling distribution and the sample size, we are able to calculate the standard deviation of the population distribution.
  • The standard deviation of the sampling distribution is called the standard error.
  • The mean of the sampling distribution will cluster around the population mean.
  • If we collect a large number of different samples mean, the distribution of those samples mean should take the shape of a normal distribution no matter what the population distribution is.

Last week, I wrote a post about the Central Limit Theorem. In that post, I explained through examples what the theorem is and why it’s so important when working with data. If you haven’t read it yet, go do it now. To keep the post short and focused, I didn’t go into many details. The goal of that post was to communicate the general concept of the theorem. In the days following it’s publication, I received many messages. People wanted me to go into more details.
Continue reading “The Theorem Every Data Scientist Should Know (Part 2) · Jean-Nicholas Hould”

Can AI and Sensors Power the Next Generation of Traffic Lights?

Can #AI and #Sensors Power the Next Generation of Traffic Lights?   #IoT

  • Like reinforcement learning, deep learning also takes inspiration from the human brain.
  • When it’s combined with reinforcement learning, it makes the search for the best solution more efficient.
  • While traffic lights do use sensors to try and make slightly more intelligent decisions than perhaps they once did, they are still fairly dumb tools for regulating the flow of traffic.
  • The researchers believe reinforcement learning provides just such a capability .
  • A recent Chinese study explores whether machine learning can do a better job.

Driverless cars are one thing, but sensor-bearing traffic lights that learn and adapt could be hitting the streets in short order. Read on to find out more.
Continue reading “Can AI and Sensors Power the Next Generation of Traffic Lights?”

The Theorem Every Data Scientist Should Know (Part 2) · Jean-Nicholas Hould

The Theorem Every Data Scientist Should Know (Part 2)  #MachineLearning #DataScience

  • The standard deviation of the population distribution is tied with the standard deviation of the sampling distribution.
  • With the standard deviation of the sampling distribution and the sample size, we are able to calculate the standard deviation of the population distribution.
  • The standard deviation of the sampling distribution is called the standard error.
  • The mean of the sampling distribution will cluster around the population mean.
  • If we collect a large number of different samples mean, the distribution of those samples mean should take the shape of a normal distribution no matter what the population distribution is.

Last week, I wrote a post about the Central Limit Theorem. In that post, I explained through examples what the theorem is and why it’s so important when working with data. If you haven’t read it yet, go do it now. To keep the post short and focused, I didn’t go into many details. The goal of that post was to communicate the general concept of the theorem. In the days following it’s publication, I received many messages. People wanted me to go into more details.
Continue reading “The Theorem Every Data Scientist Should Know (Part 2) · Jean-Nicholas Hould”

The Theorem Every Data Scientist Should Know (Part 2) · Jean-Nicholas Hould

The Theorem Every Data Scientist Should Know (Part 2)  #MachineLearning #DataScience

  • The standard deviation of the population distribution is tied with the standard deviation of the sampling distribution.
  • With the standard deviation of the sampling distribution and the sample size, we are able to calculate the standard deviation of the population distribution.
  • The standard deviation of the sampling distribution is called the standard error.
  • The mean of the sampling distribution will cluster around the population mean.
  • If we collect a large number of different samples mean, the distribution of those samples mean should take the shape of a normal distribution no matter what the population distribution is.

Read the full article, click here.


@MikeTamir: “The Theorem Every Data Scientist Should Know (Part 2) #MachineLearning #DataScience”


Last week, I wrote a post about the Central Limit Theorem. In that post, I explained through examples what the theorem is and why it’s so important when working with data. If you haven’t read it yet, go do it now. To keep the post short and focused, I didn’t go into many details. The goal of that post was to communicate the general concept of the theorem. In the days following it’s publication, I received many messages. People wanted me to go into more details.


The Theorem Every Data Scientist Should Know (Part 2) · Jean-Nicholas Hould