Introduction to Structural Equation Modeling

Introduction to Structural Equation Modeling:  #abdsc #DataScience #MachineLearning

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  • Are a Few Things You Can Do With Structural Equation Modeling
  • Structural Equation Modeling (SEM) is a statistical methodology that is widely used by researchers in the social, behavioral and educational sciences.
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  • On the psychometric side, SEM allows for latent variables with multiple indicators.

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@KirkDBorne: “Introduction to Structural Equation Modeling: #abdsc #DataScience #MachineLearning”


Structural Equation Modeling (SEM) is a statistical methodology that is widely used by researchers in the social, behavioral and educational sciences.  First i…


Introduction to Structural Equation Modeling

AI, Deep Learning, and Machine Learning: A Primer – Andreessen Horowitz

#AI, #Deep Learning, and #MachineLearning: A Primer – Andreessen Horowitz

  • Things are clearly progressing rapidly when it comes to machine intelligence.
  • From types of machine intelligence to a tour of algorithms, a16z Deal and Research team head Frank Chen walks us through the basics (and beyond) of AI and deep learning in this slide presentation.
  • Of the 20 entries they received then, the winning entry went 7.2 miles; in 2007, in the Urban Challenge, the winning entries went 60 miles under city-like constraints.
  • Now to put the fact in context, compare this to 2004, when DARPA sponsored the very first driverless car Grand Challenge.
  • “One person, in a literal garage, building a self-driving car.”

Read the full article, click here.


@MikeTamir: “#AI, #Deep Learning, and #MachineLearning: A Primer – Andreessen Horowitz”


“One person, in a literal garage, building a self-driving car.” That happened in 2015. Now to put that fact in context, compare this to 2004, when DARPA sponsored the very first driverless car Grand Challenge. Of the 20 entries they received then, the winning entry went 7.2 miles; in 2007, in the Urban Challenge, the winning entries went 60 miles under city-like constraints.


AI, Deep Learning, and Machine Learning: A Primer – Andreessen Horowitz

AI, Deep Learning, and Machine Learning: A Primer – Andreessen Horowitz

#AI, #DeepLearning, and #MachineLearning: a 45-minute video primer at  #BigData #DataScience

  • Things are clearly progressing rapidly when it comes to machine intelligence.
  • From types of machine intelligence to a tour of algorithms, a16z Deal and Research team head Frank Chen walks us through the basics (and beyond) of AI and deep learning in this slide presentation.
  • Of the 20 entries they received then, the winning entry went 7.2 miles; in 2007, in the Urban Challenge, the winning entries went 60 miles under city-like constraints.
  • Now to put the fact in context, compare this to 2004, when DARPA sponsored the very first driverless car Grand Challenge.
  • “One person, in a literal garage, building a self-driving car.”

Read the full article, click here.


@KirkDBorne: “#AI, #DeepLearning, and #MachineLearning: a 45-minute video primer at #BigData #DataScience”


“One person, in a literal garage, building a self-driving car.” That happened in 2015. Now to put that fact in context, compare this to 2004, when DARPA sponsored the very first driverless car Grand Challenge. Of the 20 entries they received then, the winning entry went 7.2 miles; in 2007, in the Urban Challenge, the winning entries went 60 miles under city-like constraints.


AI, Deep Learning, and Machine Learning: A Primer – Andreessen Horowitz

GitHub

Hands-on #machinelearning labs on @github? Oh, yeah, we got that!

  • Evaluate a Binary classification model
  • Consume the ML Web Service in a C# application
  • Comparing two binary classification model
  • Publishing a trained model as Web Service
  • Lab4 – Develop and Consume AzureML Models

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@MicrosoftR: “Hands-on #machinelearning labs on @github? Oh, yeah, we got that!”


hol-azure-machine-learning – Introduction to Machine Learning and Azure Machine Learning Services. Hands on labs to show Azure Machine Learning features, developing experiments, feature engineering, R and Python Scripting, Production stage, publishing models as web service, RRS and BES usage


GitHub

AI, Deep Learning, and Machine Learning: A Primer – Andreessen Horowitz

AI, Deep Learning, and Machine Learning: A Primer

  • Things are clearly progressing rapidly when it comes to machine intelligence.
  • From types of machine intelligence to a tour of algorithms, a16z Deal and Research team head Frank Chen walks us through the basics (and beyond) of AI and deep learning in this slide presentation.
  • Of the 20 entries they received then, the winning entry went 7.2 miles; in 2007, in the Urban Challenge, the winning entries went 60 miles under city-like constraints.
  • Now to put the fact in context, compare this to 2004, when DARPA sponsored the very first driverless car Grand Challenge.
  • “One person, in a literal garage, building a self-driving car.”

Read the full article, click here.


@republicofmath: “AI, Deep Learning, and Machine Learning: A Primer”


“One person, in a literal garage, building a self-driving car.” That happened in 2015. Now to put that fact in context, compare this to 2004, when DARPA sponsored the very first driverless car Grand Challenge. Of the 20 entries they received then, the winning entry went 7.2 miles; in 2007, in the Urban Challenge, the winning entries went 60 miles under city-like constraints.


AI, Deep Learning, and Machine Learning: A Primer – Andreessen Horowitz

AI, Deep Learning, and Machine Learning: A Primer – Andreessen Horowitz

AI, Deep Learning, and Machine Learning: A Primer (by @withfries2)

  • Things are clearly progressing rapidly when it comes to machine intelligence.
  • Of the 20 entries they received then, the winning entry went 7.2 miles; in 2007, in the Urban Challenge, the winning entries went 60 miles under city-like constraints.
  • From types of machine intelligence to a tour of algorithms, a16z Deal and Research team head Frank Chen walks us through the basics (and beyond) of AI and deep learning in this slide presentation.
  • Now to put the fact in context, compare this to 2004, when DARPA sponsored the very first driverless car Grand Challenge.
  • “One person, in a literal garage, building a self-driving car.”

Read the full article, click here.


@cdixon: “AI, Deep Learning, and Machine Learning: A Primer (by @withfries2)”


“One person, in a literal garage, building a self-driving car.” That happened in 2015. Now to put that fact in context, compare this to 2004, when DARPA sponsored the very first driverless car Grand Challenge. Of the 20 entries they received then, the winning entry went 7.2 miles; in 2007, in the Urban Challenge, the winning entries went 60 miles under city-like constraints.


AI, Deep Learning, and Machine Learning: A Primer – Andreessen Horowitz