Форум » О том, о сём » How do I learn mathematics for machine learning? » Ответить
How do I learn mathematics for machine learning?
Priyasingh: To learn mathematics for machine learning, I recommend the following steps: Develop a strong foundation in linear algebra. This includes understanding concepts like vectors, matrices, eigenvalues/eigenvectors, and matrix decompositions. Textbooks like "Introduction to Linear Algebra" by Gilbert Strang are excellent resources. Study calculus, particularly multivariate calculus. This will provide the necessary background for understanding optimization techniques used in machine learning models. Textbooks like "Calculus" by James Stewart cover these topics well. Familiarize yourself with probability and statistics. This includes discrete and continuous probability distributions, expectation, variance, correlation, and hypothesis testing. Textbooks like "Introduction to Probability" by Dimitri Bertsekas and John Tsitsiklis are good options. Learn about optimization methods, such as gradient descent, Newton's method, and constrained optimization. These techniques are fundamental to training machine learning models. Resources like "Convex Optimization" by Stephen Boyd and Lieven Vandenberghe can provide a solid grounding. Explore topics in algorithmic thinking and computational complexity. Understanding how to design efficient algorithms and analyze their time and space complexity is crucial for implementing machine learning models at scale. Introduction to Algorithms by Thomas Cormen et al. is a comprehensive reference. Visit here-Machine Learning Training in Pune
Ответов - 0
полная версия страницы