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Google Colab

Last updated: Jun 20, 2024

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What Is Google Colab?

Google Colab is a cloud platform that can help users write and run Phyton code on browsers.

Google Colaboratory does not need any configuration or access, thus making it easy for users to share and allowing it to be accessed at no cost to GPU (Graphics Processing Unit). Moreover, the GPUs provided are very diverse.

This Google Research platform has helped many students, AI researchers, data scientists, developers, and machine learning engineers to work on and access computing free at no charge.

In general, Google Colab looks similar to the Jupyter Notebook which can also run on browsers. With that being said, users no longer need to install or do any setup on the computer since the cloud has provided all the settings and adjustments needed.

In addition, you can also you the Notebook feature allowing you to edit and give access to all the teams you work with. Another special thing, your team can also make some comments on it!

The Notebook can combine several codes and rich texts within one document, including the HTML, LaTex, pictures, and other elements.

Since this platform has been integrated with Google, you can also access the Notebook you have been working on through your Google Drive account.

The Benefits of Using the Google Colab

We have answered your curiosity about what is Google Colab. Now, you should know that Google Colab is an integrated platform that can give you many valuable benefits, such as:

1. You Can Access it Without Configuration

You can access Google Colab directly, without the configuration step. With that being said, you can directly write and run your code on the Notebook. You even don’t need to reorganize your development environment with this platform.

2. Free Access to the Computing Resources

One of the most applicable benefits of Google Colab is you can access the computing resources without any expensive infrastructure since this platform provides free access to the GPU and the TPU.

This means you can save time and budget as you can directly work on the Notebook.

3. Integrated with GitHub and Google Drive

Google Colab is integrated with GitHub and Google Drive so that users can directly save and access their work on the cloud.

4. Real-Time Team Collaboration

Users can share their Notebook with other team members to enhance efficiency and collaborate together at the same exact time.

5. Easy Access to Machine Learning Library

Machine learning engineers can access any machine learning library on Google Colab, e.g. PyTorch and TensorFlow. With this feature, users can run the machine learning project without the need to configure their library.

How to Use the Google Colab

Now that you understand what is Google Colab and its benefits, let’s learn how to use it with the following steps.

1. Make a Folder for Google Colab

The first step of using Google Collaboratory is to make a folder on Google Drive. As mentioned before, Google Colab is integrated with other Google features like Google Drive, so you can access it with your Google account.

That means, before using the Google Colab, you need to ensure you have an active Google account. Without an active account, you will not be able to access many parts of Colaboratory features.

2. Make a Notebook

Next, you can make a Notebook by right-clicking in the folder > More > Colaboratory. You can also access it from New > More > Colaboratory.

Google Colab Examples

If you know what is Google Colab, you must understand that any major and profession from researchers to students, can use it to make their work a lot more efficient.

Here are some Google Colab examples you should know:

1. Simulation and Statistical Modeling

What is Google Colab? It is a Google platform that can be used by educators or statistic and math experts to make simulations and statistical modeling. They can access SciPy and NumPy in the library to run predictive analysis on the statistical model.

2. Big Data Management

Users can also use the GPU and TPU from Google Colab to manage data in large quantities. In such cases, users can use Pandas to help with data manipulation and data cleaning, or Matplotlib to interpret the results.

3. Machine Learning Model Development

The last Google Colab example you can see is on the development process of the machine learning model. You can use this platform to import data, use the algorithm to test the model, and eventually evaluate its performance.