Mathematics for Machine Learning and LLMs

Original PDF نویسندگان: Michel Janos

جزئیات

فرمت: Original PDF صفحات: 631 pages تاریخ انتشار نسخه الکترونیکی : October 29, 2024 لینک: https://www.amazon.com/dp/B0DLHDJLXY

توضیحات

ML is a subset of AI that focuses on developing algorithms that allow computers to learn from and make predictions or decisions based on data. It involves training models on large datasets to recognize patterns and improve their performance over time without being explicitly programmed for each task.ML is undoubtedly one of the most cutting-edge technologies of our time!If you are starting out with ML and want to become a Data Scientist you need to understand the mathematics behind ML algorithms. Mathematics is the backbone of data science and AI, providing the essential tools and frameworks needed to analyze data, develop algorithms, and build models.There's no getting around it. It's an intrinsic part of a Data Scientist's role and any recruiter or experienced professional will attest to that.The enthusiast who is interested in learning more about the magic behind ML algorithms currently faces a daunting set of prerequisites: programming, large-scale Data Analysis, mathematical structures associated with models and the actual knowledge of the application in focus. For historical reasons, ML courses tend to be taught in the Computer Science department, where students are often trained in programming and data analysis, but not so much in mathematics and statistics.However, this book is not designed to make you a mathematician.It aims to help you learn some fundamental concepts and the notation used to express them. The book provides a practical approach to working with data and focuses on the key mathematical concepts you will encounter in ML studies. It is designed to fill in the gaps for students who have missed out on these key concepts as part of their formal education, or who need to refresh themselves after a long break from studying mathematics.Understanding the mathematical principles behind machine learning models allows data scientists and AI students not only to implement algorithms but also to troubleshoot, improve, and innovate. This knowledge also helps them make informed decisions about model selection and adjustments, ensuring that their solutions are robust, interpretable, and reliable.But why learn the mathematics behind ML algorithms when we can easily use widely available libraries to build models?It's true that as ML becomes more ubiquitous and its software packages become easier to use, it's natural and desirable for the technical details at the mathematical level to be left in the background. But make no mistake. Without understanding the mathematics behind it, you're unlikely to be hired as a Data Scientist.I try to develop mathematical concepts with as few formulas as possible, focusing on mathematical topics associated with specific ML applications. That's why my premise is that you have some grasp of the fundamental concepts of ML, like Regression and Classification, as wellas, some familiarity in using Large Language Models..This book focuses on concepts and the examples are developed in Python because as a Data Scientist candidate this is one of the things you can't afford not to know.Mathematics is not just a tool but a language that enables data scientists and AI students to understand, model, and solve complex problems. It provides the theoretical foundation and practical techniques necessary to navigate the data-driven world and to harness the power of AI effectively.

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