Remarks here are personal and this is just for practice purposes only. Equations may seem erratic at places, as LATEX syntaxes are used to separate the lines. Works better on colab
Lessons here are from the book titled:
Written by Thomas Nield
© Published by O'REILLY Media®
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Chapter Length Consideration: Kindly note that the chapters within this book are deliberately extensive, allowing you ample time to absorb the content at your own pace.
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Utilization of Python's Sympy Library: It is worth highlighting that this book heavily relies on the Sympy library within the Python programming language.
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Emphasis on Comprehensive Understanding: While the comprehensive nature of this book may lead to a temptation to skim through certain sections, particularly those involving intricate algorithms and lengthy lines of code, I strongly encourage you not to succumb to this temptation. Engaging directly with these aspects not only reinforces your cognitive assimilation of the material but also facilitates a deeper understanding of the subject matter.
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Value of In-Depth Exploration: Even seemingly straightforward algorithms such as Linear Regression demand a meticulous approach. It's essential to invest time in critically evaluating results and formulating your own hypotheses.
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Importance of Practical Implementation: While there's no shortcut to mastering the content, consider the option of copying and executing the provided codes within the chapters. This approach can enhance your familiarity with coding practices. However, I advise against adopting the same approach for the exercises, as they hold significant learning value when approached through manual coding.
Your commitment to engaging with the material in a comprehensive manner will undoubtedly contribute to a more profound grasp of the subject matter. Thank you for your dedication to this learning journey.
⚠ This is a companion repository for the book.