Google Colab https // : What is Google colab used for?

Google Colab https // : What is Google colab used for?

Welcome to this Colab where you will get a quick introduction to the Python programming language and the environment used for the course’s exercises: Colab.

Colab is a Python development environment that runs in the browser using Google Cloud.

For example, to print “Hello World”, just hover the mouse over [ ] and press the play button to the upper left. Or press shift-enter to execute.

print(“Hello World”)

What is Colab?

Colab, or ‘Colaboratory’, allows you to write and execute Python in your browser, with

  • Zero configuration required
  • Access to GPUs free of charge
  • Easy sharing

Whether you’re a student, a data scientist or an AI researcher, Colab can make your work easier. Watch Introduction to Colab to find out more, or just get started below!

Google Colab Tutorial

Getting started

The document that you are reading is not a static web page, but an interactive environment called a Colab notebook that lets you write and execute code.

For example, here is a code cell with a short Python script that computes a value, stores it in a variable and prints the result:

seconds_in_a_day = 24 * 60 * 60

To execute the code in the above cell, select it with a click and then either press the play button to the left of the code, or use the keyboard shortcut ‘Command/Ctrl+Enter’. To edit the code, just click the cell and start editing.

Variables that you define in one cell can later be used in other cells:

seconds_in_a_week = 7 * seconds_in_a_day

Machine learning

With Colab you can import an image dataset, train an image classifier on it, and evaluate the model, all in just a few lines of code. Colab notebooks execute code on Google’s cloud servers, meaning you can leverage the power of Google hardware, including GPUs and TPUs, regardless of the power of your machine. All you need is a browser.

Colab is used extensively in the machine learning community with applications including:

  • Getting started with TensorFlow
  • Developing and training neural networks
  • Experimenting with TPUs
  • Disseminating AI research
  • Creating tutorials

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