Google’s new machine learning API recognizes objects in videos

Google’s new machine learning API recognizes objects in videos

  • At its Cloud Next conference in San Francisco, Google today announced the launch of a new machine learning API for automatically recognizing objects in videos and making them searchable.
  • The new Video Intelligence API will allow developers to build applications that can automatically extract entities from a video.
  • Until now, most similar image recognition APIs available in the cloud only focused on doing this for still images, but with the help of this new API, developers will be able to build applications that let users search and discover information in videos.
  • Besides extracting metadata, the API allows you to tag scene changes in a video.

Google’s new machine learning API recognizes objects in videos
Continue reading “Google’s new machine learning API recognizes objects in videos”

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

  • 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”

Outlier App: An Interactive Visualization of Outlier Algorithms

Outlier App (w/ code): Interactive Visualization Of Outliers  #DataScience #machinelearning

  • A density based algorithm can also select different outliers versus a distance based algorithm.
  • You can see how outliers differ when scaling is used with kmeans.
  • The source code for the outlier app is on github .
  • DataScience+ Learn R programming for data science
  • I built a shiny app that allows you to play around with various outlier algorithms and wanted to share it with everyone.

I was recently trying various outlier detection algorithms. For me, the best way to understand an algorithm is to tinker with it. I built a shiny app that allows you
Continue reading “Outlier App: An Interactive Visualization of Outlier Algorithms”

GitHub

.@mza announcing a new deeplearning benchmark:  #reInvent

  • To run comparisons in a deep learning cluster created with CloudFormation
  • The runscalabilitytest.sh script runs scalability tests and records the throughput as images/sec in CSV files under ‘csv_*’ directories.
  • Step 1: Create a deep learning cluster using CloudFormation .
  • Scalability Comparison Scripts for Deep Learning Frameworks

Contribute to deeplearning-benchmark development by creating an account on GitHub.
Continue reading “GitHub”