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Torf A. Practical Linear Algebra For Machine Learning 2020
Whether you want to learn Machine Learning for your work or research or you want to become a master, so the others pay you to do it, you need to know how it works. For knowing how it works, you NEED TO KNOW Linear Algebra, which is the foundation of Machine Learning. BUT Linear Algebra is too boundless! In this book, you will get what is NECESSARY. Machine Learning is everywhere these days and a lot of fellows desire to learn it and even master it! This burning desire creates a sense of impatience. We are looking for shortcuts and willing to ONLY jump to the main concept. If you do a simple search on the web, you see thousands of people asking ”How can I learn Machine Learning?”, ”What is the fastest approach to learn Machine Learning?”, and ”What are the best resources to start Machine Learning?”Well, there is a problem here. Mastering a branch of science is NOT just a feel-good exercise. It has its own requirements. One of the most critical requirements for Machine Learning is Linear Algebra. Basically, the majority of Machine Learning is working with data and optimization. How can you want to learn those without Linear Algebra? How would you process and represent data without vectors and matrices? On the other hand, Linear Algebra is a branch of mathematics after all. A lot of people trying to avoid mathematics or have the temptation to” just learn as necessary.” I agree with the second approach, though. However, the bad news is: You cannot escape Linear Algebra if you want to learn Machine Learning and DeepLearning. There is NO shortcut. Introduction Basic Linear Algebra Definitions An Introduction to NumPy Matrix Operations Vector and Matrix Norms Linear Independence Matrix Decomposition
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