A Visual Introduction to Machine Learning

A Visual Introduction to #MachineLearning #abdsc

  • Using a data set about homes, we will create a machine learning model to distinguish homes in New York from homes in San Francisco.
  • Let’s say you had to determine whether a home is in San Francisco or in New York.
  • In machine learning terms, categorizing data points is a classification task.Since San Francisco is relatively hilly, the elevation of a home may be a good way to distinguish the two cities.
  • Based on the home-elevation data to the right, you could argue that a home above 240 ft should be classified as one in San Francisco.
  • The data suggests that, among homes at or below 240 ft, those that cost more than $1776 per square foot are in New York City.

This article was written by Stephanie and Tony on R2D3. 
In machine learning, computers apply statistical learning techniques to automatically identify pattern…
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The Gentlest Introduction to Tensorflow – Part 3

The Gentlest Introduction to #Tensorflow – Part 3  #NeuralNetworks #MachineLearning

  • To do that we:

    In reality, any prediction relies on multiple features, so we advance from single-feature to 2-feature linear regression; we chose 2 features to keep visualization and comprehension simple, but the concept generalizes to any number of features.

  • In the single-feature scenario, we had to use linear regression to create a straight line to help us predict the outcome ‘house size’, for cases where we did not have datapoints.
  • Recall for a single-feature (see left of image below), the linear regression model outcome (y) has a weight (W), a placeholder (x) for the ‘house size’ feature, and a bias (b).
  • In TF, this multiplication would be:

    Note: The x representations in the feature matrix become more complex, i.e., we use x1.1, x1.2, instead of x1, x2, etc. because the feature matrix (the one in the middle) has expanded from representing a single datapoint of n-features (1 row x n columns) to representing m datapoints with n-features (m rows x n columns), so we extended x, e.g., x1, to x.

  • In TF, they would be written as:

    In TF, with our x, and W represented in matrices, regardless of the number of features our model has or the number of datapoints we want to handle, it can be simplified to:

    We do a side-by-side comparison to summarize the change from single to multi-feature linear regression:

    We illustrated the concept of multi-feature linear regression, and showed how we extend our model and TF code from single to 2-feature linear regression models, which is generalizable to n-feature models.


This post is the third entry in a series dedicated to introducing newcomers to TensorFlow in the gentlest possible manner. This entry progresses to multi-feature linear regression.

Continue reading “The Gentlest Introduction to Tensorflow – Part 3”

A Visual Introduction to Machine Learning

A Visual Introduction to #MachineLearning #abdsc

  • Using a data set about homes, we will create a machine learning model to distinguish homes in New York from homes in San Francisco.
  • Let’s say you had to determine whether a home is in San Francisco or in New York.
  • In machine learning terms, categorizing data points is a classification task.Since San Francisco is relatively hilly, the elevation of a home may be a good way to distinguish the two cities.
  • Based on the home-elevation data to the right, you could argue that a home above 240 ft should be classified as one in San Francisco.
  • The data suggests that, among homes at or below 240 ft, those that cost more than $1776 per square foot are in New York City.

This article was written by Stephanie and Tony on R2D3. 
In machine learning, computers apply statistical learning techniques to automatically identify pattern…
Continue reading “A Visual Introduction to Machine Learning”

A Visual Introduction to Machine Learning

A Visual Introduction to #MachineLearning #abdsc

  • You need to be a member of Data Science Central to add comments!
  • In machine learning, computers apply statistical learning techniques to automatically identify patterns in data.
  • The data suggests that, among homes at or below 240 ft, those that cost more than $1776 per square foot are in New York City.
  • Using a data set about homes, we will create a machine learning model to distinguish homes in New York from homes in San Francisco.
  • There are clearly patterns in the data, but the boundaries for delineating them are not obvious.

This article was written by Stephanie and Tony on R2D3. 
In machine learning, computers apply statistical learning techniques to automatically identify pattern…
Continue reading “A Visual Introduction to Machine Learning”

Google is using machine learning to reduce the data needed for high-resolution images

Google is using machine learning to reduce the data needed for high-resolution images

  • Doing so reduces the data cost of each image by up to 75 percent, says Google.
  • While traditional upsampling uses fixed rules to work out which new pixels to use where, RAISR adapts its methods to each image.
  • The company says its techniques reduce data costs up to 75 percent per image
  • Last November, Google unveiled a prototype technology called RAISR that uses machine learning to make low-resolution images appear more detailed.
  • Google is using machine learning to reduce the data needed for high-resolution images

Last November, Google unveiled a prototype technology called RAISR that uses machine learning to make low-resolution images appear more detailed. Now, the company has begun the process of…
Continue reading “Google is using machine learning to reduce the data needed for high-resolution images”

Google reveals RAISR: an image enhancement tech which uses machine learning

#google reveals RAISR: an image enhancement tech which uses machine learning

  • Google reveals RAISR: an image enhancement tech which uses machine learning
  • The best way to stay connected to the Android pulse everywhere.
  • Google Research Scientist Peyman Milanfar explained the technology on the Google research blog and how it differs from existing methods image enhancement methods.
  • Google Photos now allows you to create animations offline
  • Scott Adam Gordon is a European correspondent for Android Authority .

Google has shared details on RAISR, its new image enhancement technology which uses machine learning to produce high-quality versions of low-quality images.
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Google Brings Machine Learning to Google Play Music

Machine learning is coming to Google Play Music -

  • Google Brings Machine Learning to Google Play Music
  • Click “Add Devices” to add your devices for quick access !
  • Presenting you with music you like isn’t enough for Google though, they wanted to take it a step further.
  • Google has just announced a brand new update to their music streaming service, Google Play Music.
  • You can either wait patiently for the update to be pushed to your device, or you can sideload the update when it becomes available.

Google has just announced a brand new update to their music streaming service, Google Play Music. The new design and features will begin rolling out to Android, iOS and the web starting this week, and Google says it will be made available to 62 countries around the world.
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