What you would learn in Calculus for Machine Learning LiveLessons course?
Calculus used in Machine Learning LiveLessons introduces the mathematical area of calculus, which is the study of rates of change from the beginning. It is crucial because computing derivatives through differentiation is the foundation of optimizing most machine learning techniques, such as those employed in deep learning like stochastic gradient descent and backpropagation. Through the careful explanation of the theory and practical examples, you'll learn how calculus is utilized to calculate limits and distinguish functions. Additionally, you'll learn how to use automatic differentiation to the well-known TensorFlow 2 and PyTorch machine learning libraries. The lessons will build upon single-variable derivative calculus to describe the learning gradients (made possible by partial-derivative calculation) along with integral calculus (which determines the area under a curve and can be helpful for many tasks related to machine learning).
Course Content:
--Develop an understanding of what’s going on beneath the hood of machine learning algorithms, including those used for deep learning.
--Compute the derivatives of functions, including by using AutoDiff in the popular TensorFlow 2 and PyTorch libraries.
--Be able to grasp the details of the partial-derivative, multivariate calculus ordinary in machine learning papers and many other subjects that underlie ML, including information theory and optimization algorithms.
--Use integral calculus to determine the area under any given curve; a recurring task in ML applied, for example, to evaluate model performance by calculating the ROC AUC metric.
Download Calculus for Machine Learning LiveLessons from below links NOW!
