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Data Science with .NET and Polyglot Notebooks

You're reading from   Data Science with .NET and Polyglot Notebooks Programmer's guide to data science using ML.NET, OpenAI, and Semantic Kernel

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Product type Paperback
Published in Aug 2024
Publisher Packt
ISBN-13 9781835882962
Length 404 pages
Edition 1st Edition
Languages
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Author (1):
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Matt Eland Matt Eland
Author Profile Icon Matt Eland
Matt Eland
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Table of Contents (22) Chapters Close

Preface 1. Part 1: Data Analysis in Polyglot Notebooks FREE CHAPTER
2. Chapter 1: Data Science, Notebooks, and Kernels 3. Chapter 2: Exploring Polyglot Notebooks 4. Chapter 3: Getting Data and Code into Your Notebooks 5. Chapter 4: Working with Tabular Data and DataFrames 6. Chapter 5: Visualizing Data 7. Chapter 6: Variable Correlations 8. Part 2: Machine Learning with Polyglot Notebooks and ML.NET
9. Chapter 7: Classification Experiments with ML.NET AutoML 10. Chapter 8: Regression Experiments with ML.NET AutoML 11. Chapter 9: Beyond AutoML: Pipelines, Trainers, and Transforms 12. Chapter 10: Deploying Machine Learning Models 13. Part 3: Exploring Generative AI with Polyglot Notebooks
14. Chapter 11: Generative AI in Polyglot Notebooks 15. Chapter 12: AI Orchestration with Semantic Kernel 16. Part 4: Polyglot Notebooks in the Enterprise
17. Chapter 13: Enriching Documentation with Mermaid Diagrams 18. Chapter 14: Extending Polyglot Notebooks 19. Chapter 15: Adopting and Deploying Polyglot Notebooks 20. Index 21. Other Books You May Enjoy

Extending notebooks with kernels

In our example in the previous section, we saw how code cells could be executed to produce a result.

This work is carried out by a kernel designed for the programming language you’re working with. In the earlier example, we saw a code cell written in Python. Since this is Python code, it uses the Python kernel.

In this way, the Jupyter Notebooks environment can support multiple languages, including Julia, Python, and R, as shown in Figure 1.8:

Figure 1.8 – Different kernels available to the notebook

Figure 1.8 – Different kernels available to the notebook

When you execute a Python cell, the code is sent to the Python kernel, which interprets it, executes it, and produces a result as illustrated in Figure 1.9:

Figure 1.9 – Jupyter Notebooks executing the Python kernel

Figure 1.9 – Jupyter Notebooks executing the Python kernel

This extensible design allows Jupyter Notebooks to support additional languages by adding additional kernels for those languages.

This is exactly what Polyglot...

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