![]() ![]() ![]() In the class, we identified the newly added feature which is “data_table formatter” in Google colab, let me explain with an example.Įnable_dataframe_formatter() is a function provided by the _table module in Google Colab. It's a great way to get started with machine learning and data science without the need for expensive hardware or complex software setups. Google Colab is a powerful tool for machine learning and data science, providing a cloud-based environment for writing and executing Python code, collaborating with others, and leveraging advanced computing resources. You can select your desired hardware from the "Runtime" menu, and Google Colab will provide the necessary resources to run your code. One of the key features of Google Colab is the ability to use GPU and TPU resources for accelerated computing. You can also add text, images, and videos to your notebook to provide context and documentation. The notebook environment provides a range of features, including the ability to add comments, create headings, and insert code blocks. Once you log in to Colab, you can create a new notebook, which is essentially a Python script that you can run on the Colab servers. To get started with Google Colab, all you need is a Google account. It also offers the ability to import and export data from various sources, including Google Drive and GitHub. Google Colab is a popular tool for machine learning and data science, as it provides a range of pre-installed libraries and tools such as TensorFlow, Keras, and PyTorch. It also provides a great way to share and collaborate on projects with others in a convenient and efficient way. One of the key benefits of using Google Colab is that it eliminates the need for users to set up their own computing environment and can save time and resources when working on complex projects. It provides free access to computing resources, including GPUs and TPUs, and allows users to collaborate with others in real-time. Google Colab Is a cloud-based development environment that allows users to write and execute Python code using a web browser. ![]() ![]() In the next chapter, we will see how to save your work.On February 22, 2023, in our data analysis class, I explained the importance of Google colab, PANDAS function, and the basic of data preparation functions. $e^x = \sum_(cos(2\pi x_1) + cos(2\pi x_2))$ĭescribing full markup syntax is beyond the scope of this tutorial. Let us try another more complicated equation as shown here − Hit Enter and the markdown code disappears from the text cell and only the rendered output is shown. You will see the immediate rendering of the markdown code in the right hand side panel of the text cell. Mathematical EquationsĪdd a Text Cell to your notebook and enter the following markdown syntax in the text window − The output of the above commands is rendered on the right hand side of the Cell as shown here. Type in the following text in the Text cell. Let us look into few examples of markup language syntax to demonstrate its capabilities. Let us now see you how to add text cells to your notebook and add to it some text containing mathematical equations. Text Cells are formatted using markdown - a simple markup language. Colab provides Text Cells for this purpose.Ī text cell containing few mathematical equations typically used in ML is shown in the screenshot below −Īs we move ahead in this chapter, we will see the code for generating the above output. ML heavily uses mathematics and to explain those terms and equations to your readers you need an editor that supports LaTex - a language for mathematical representations. However, many a time you need more than a simple text based comments to illustrate the ML algorithms. As the code cell supports full Python syntax, you may use Python comments in the code window to describe your code. ![]()
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