Titlebook: Beginner‘s Guide to Streamlit with Python; Build Web-Based Data Sujay Raghavendra Book 2023 Sujay Raghavendra 2023 Artificial Intelligence.

Devastate

https://doi.org/10.1007/978-1-4842-8983-9Artificial Intelligence; Machine Learning; Python; Data Science; Natural Language Processing; Data Visual

FUSC

978-1-4842-8982-2Sujay Raghavendra 2023

guzzle

https://doi.org/10.1007/978-3-662-49451-6In the first chapter, we saw a complete overview of Streamlit with its core features and how it is different from its competitors in the data science and machine learning domain. We also discussed the installation steps that are necessary on various platforms and created our very own Streamlit application.

脆弱吧

Hyperbolic Systems of Balance Laws,In the previous chapter, we saw how data in the form of text and tables can be explored in Streamlit. In this chapter, we will learn what data visualization is. We will also explore the Streamlit functions to visualize data. Later, we will see the flexibility of how to use Streamlit applications with various Python libraries to visualize data.

拘留

https://doi.org/10.1007/978-3-642-04048-1In this chapter, we will focus on data that we can see and hear. This type of data is in the form of images, audio, videos, or animations. This is known as .. An application may contain any one or a combination of these types of data.

努力趕上

原來

https://doi.org/10.1007/978-3-662-22019-1In this chapter, we will discuss how to set up columns, layouts, and navigation into which we can insert elements. These will help to divide our application into grids. The navigation is used when we need more than one page in our application. We will also explore how to use sidebars. Let’s get started with columns.

COUCH

Counteract

https://doi.org/10.1007/978-3-642-87025-5In this final chapter of the book, we will see one small example of a computer vision application that can predict what animals are in an image. This application will deploy our trained model to classify animal images.

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