Write and Execute Python Coding with Google Colab

Blog Tuesday June 8th, 2021

Write and Execute Python Coding with Google Colab


If you are a programmer who is currently into python programming, then don’t forget to check on Google Colab. Google Colab, which is short for Google Colaboratory, is a program of Google Research.

Google Colab allows anyone to write and execute arbitrary python code via a browser. Google claims this product is perfect for machine learning, data analytics, and education. The cool thing is, Google provides Google Colab for free.

The advantages of Google Colab are that there is no need for configuration, free access to GPU (General Processing Units), and easy sharing.

Google Colab can be used for anyone no matter what their background is. Whether you are a student, data scientist, or AI researcher.

Python programmers can make use of an interactive environment called Colab Notebook to write and execute code. With Colab Notebook, you can combine executable code and rich text in one single document, along with images, HTML, LaTeX, and many more.

The Colab Notebook you create can be stored in Google Drive, and you can easily share them with colleagues or friends. Friends who gain access to the file can comment or even edit directly. Colab Notebook actually is Jupyter Notebook, but the organizer is Google Colab.

For Data Science, Google Colab can help you to use the Python library to analyze and visualize data. Import your data directly into Colab Notebook via your Google Drive account, spreadsheets, Github, and others.

For Machine Learning, Google Colab can help you do many things such as importing image datasets, training image classifiers, and evaluating models, all of which can be done with just a few codes.

Colab Notebook executes code on Google Cloud servers, this means that you have wide access to take advantage of Google hardware, including GPUs and TPUs, without having to think about the power of your computer machine because what is needed is a browser.

Colab Notebook is widely used by the machine learning community whose applications include the following:

  1. TensorFlow
  2. Developing and training Neural networks
  3. Experiment with TPU
  4. Deploying AI research
  5. Creating tutorial


Photo by Joshua Reddekopp on Unsplash

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