Informit – Calculus for Machine Learning LiveLessons (Video Training) 2021-1

Published on: 2021-06-10 20:25:54

Categories: 28

Description

Calculus for Machine Learning LiveLessons (Video Training) is an algebra training course for machine learning. These topics are necessary because derivative computations using optimization-based derivatives often include machine learning patterns (including those used in deep learning, such as backpropagation and stochastic gradient descent). As you learn this theory, you will gain a functional understanding of how to use algebra to calculate limit and derivation functions.

What you will learn in the Calculus for Machine Learning LiveLessons (Video Training) course:

Develop an understanding of how machine learning algorithms work, including those used in deep learning.
Calculate derivatives of functions using AutoDiff in the popular TensorFlow 2 and PyTorch libraries
Ability to understand partial (relative derivative) information, multivariate algebra that is widely used in machine learning, and other topics in the machine learning subset including information theory and optimization algorithms.
Use integrals to determine the area under a curve, for example calculating the area under a ROC curve to evaluate model performance

Course specifications

Publisher: Informit
Instructors: Jon Krohn
Language: English
Level: Medium
Courses: 58
Duration: 6 hours and 14 minutes

Course topics:

Lesson 1: Orientation to Calculus

Lesson 2: Limits

Lesson 3: Differentiation

Lesson 4: Advanced Differentiation Rules

Lesson 5: Automatic Differentiation

Lesson 6: Partial Derivatives

Lesson 7: Gradients

Lesson 8: Integrals

Course prerequisites:

Mathematics: Familiarity with secondary school-level mathematics will make the class easier to follow. 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.

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.