The process for detecting that activity status via ML is very similar, only the axes are different. They act on data and your program provides answers**.** In the case of the activity detection, the rules (the code you wrote to define activity types) acted upon the data (the person's movement speed) to produce an answer: the return value from the function for determining the activity status of the user (whether they were walking, running, biking, or doing something else). You express rules in a programming language. Install NumPy here.Ĭonsider the traditional manner of building apps, as represented in the following diagram:
BASIC PROGRAMMING LANGUAGE HELLO WORLD INSTALL
You can learn more about and install TensorFlow here. You'll also need TensorFlow and the NumPy library. If you're using a different IDE, make sure you have Python installed. You can find the code for the rest of the codelab running in Colab. If you've never created an ML model using TensorFlow, you can use Colaboratory, a browser-based environment containing all the required dependencies. It's extremely difficult to write a program that will recognize the golfing activity, so what do you do? You can use ML to solve the problem! Prerequisitesīefore attempting this codelab, you'll want to have:
BASIC PROGRAMMING LANGUAGE HELLO WORLD HOW TO
It's less obvious how to create a rule to determine the activity. Now, consider what happens when you want to include an activity, like golf. In a final condition, you could similarly detect cycling. You could extend that to running with another condition. You might have access to the speed at which a person is walking and attempt to infer their activity based on that speed using a conditional. In this codelab, you'll learn the basic "Hello, World" of ML, where instead of programming explicit rules in a language, such as Java or C++, you'll build a system trained on data to infer the rules that determine a relationship between numbers.Ĭonsider the following problem: You're building a system that performs activity recognition for fitness tracking.
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