- #ACTIVITY FOR UNDERSTANDING LIMITS IN CALCULUS HOW TO#
- #ACTIVITY FOR UNDERSTANDING LIMITS IN CALCULUS SOFTWARE#
- #ACTIVITY FOR UNDERSTANDING LIMITS IN CALCULUS CODE#
Programming: All code demos are in Python, so experience with it or another object-oriented programming language would be helpful for following along with the hands-on examples. If you are comfortable dealing with quantitative information, such as understanding charts and rearranging simple equations, you should be well prepared to follow along with all the mathematics.
#ACTIVITY FOR UNDERSTANDING LIMITS IN CALCULUS SOFTWARE#
Who Should Take This Course -People who use high-level software libraries (e.g., scikit-learn, Keras, TensorFlow) to train or deploy machine learning algorithms and would like to understand the fundamentals underlying the abstractions, enabling them to expand their capabilities -Software developers who would like to develop a firm foundation for the deployment of machine learning algorithms into production systems -Data scientists who would like to reinforce their understanding of the subjects at the core of their professional discipline -Data analysts or AI enthusiasts who would like to become data scientists or data/ML engineers, and so are keen to deeply understand the field they’re entering from the ground up (a very wise choice!) Course Requirements -Mathematics: Familiarity with secondary school–level mathematics will make the class easier to follow along with. 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.
Be able to grasp the details of the partial-derivative, multivariate calculus that is common in machine learning papers and in many other subjects that underlie ML, including information theory and optimization algorithms. Compute the derivatives of functions, including by using AutoDiff in the popular TensorFlow 2 and PyTorch libraries.
#ACTIVITY FOR UNDERSTANDING LIMITS IN CALCULUS HOW TO#
Skill Level Intermediate Learn How To -Develop an understanding of what’s going on beneath the hood of machine learning algorithms, including those used for deep learning. Later lessons build on single-variable derivative calculus to detail gradients of learning (which are facilitated by partial-derivative calculus) and integral calculus (which determines the area under a curve and comes in handy for myriad tasks associated with machine learning). You’ll also learn how to apply automatic differentiation within the popular TensorFlow 2 and PyTorch machine learning libraries. Through the measured exposition of theory paired with interactive examples, you’ll develop a working understanding of how calculus is used to compute limits and differentiate functions. It is essential because computing derivatives via differentiation is the basis of optimizing most machine learning algorithms, including those used in deep learning such as backpropagation and stochastic gradient descent. 6+ Hours of Video Instruction An introduction to the calculus behind machine learning models Overview Calculus for Machine Learning LiveLessons introduces the mathematical field of calculus-the study of rates of change-from the ground up.
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