Google Colaboratory
Shitalkumar R. Sukhdeve, Sandika S. Sukhdeve
Abstract
In this chapter, we will explore Google Colaboratory, a cloud-based Jupyter notebook environment that will be freely provided by Google. We will discover its key features, including its cloud-based nature, user-friendly interface, integration with Google Cloud Platform (GCP) services, sharing and collaboration capabilities, and support for GPU and TPU computing. These features will make Colaboratory an accessible and powerful tool for data scientists, software engineers, and students. We will delve into the process of creating and running Jupyter notebooks on Google Colaboratory. We will learn how to access the platform, sign up for a Google account, and navigate the Colab interface. We will also understand how to create new Colaboratory notebooks, select the runtime type, and start coding in Python. Through hands-on examples, we will practice inserting text, performing arithmetic operations, generating random numbers, and visualizing data using libraries like Matplotlib and Seaborn. Moreover, we will explore importing libraries into Colab notebooks and working with data stored in Google Drive using the google.colab library. We will learn how to import data from and write data to Google Drive, as well as access data in Google Drive through Colaboratory. Additionally, we will briefly touch upon running machine learning models on Google Colaboratory and mention the availability of powerful machine learning tools and libraries like TensorFlow and Keras. We will even explore an example of building a machine-learning model using the scikitlearn library. In conclusion, this chapter will provide an overview of Google Colaboratory and its features, guide us through the process of accessing and using Colab, and teach us various coding, data manipulation, and visualization techniques within the Colaboratory environment.