The Resource Calculus for Machine Learning LiveLessons, Krohn, Jon

Calculus for Machine Learning LiveLessons, Krohn, Jon

Label
Calculus for Machine Learning LiveLessons
Title
Calculus for Machine Learning LiveLessons
Statement of responsibility
Krohn, Jon
Creator
Contributor
Author
Subject
Genre
Language
  • eng
  • eng
Summary
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. 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. 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. You'll also learn how to apply automatic differentiation within the popular TensorFlow 2 and PyTorch machine learning libraries. 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). 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. --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 that is common in machine learning papers and in 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. 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 ver..
Characteristic
videorecording
http://library.link/vocab/creatorName
Krohn, Jon
http://library.link/vocab/relatedWorkOrContributorName
O'Reilly Media Company
Label
Calculus for Machine Learning LiveLessons, Krohn, Jon
Link
https://databases.mvlc.org/connect/oreilly?ID=9780137398171
Instantiates
Publication
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
two-dimensional moving image
Content type code
  • tdi
Content type MARC source
rdacontent
Control code
oreilly9780137398171
Dimensions
unknown
Edition
1st edition
Extent
1 online resource (1 video file, approximately 6 hr., 13 min.)
Issuing body
Made available through: O'Reilly Media Company.
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Reproduction note
Electronic reproduction.
Specific material designation
remote
System details
Mode of access: World Wide Web
Label
Calculus for Machine Learning LiveLessons, Krohn, Jon
Link
https://databases.mvlc.org/connect/oreilly?ID=9780137398171
Publication
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
two-dimensional moving image
Content type code
  • tdi
Content type MARC source
rdacontent
Control code
oreilly9780137398171
Dimensions
unknown
Edition
1st edition
Extent
1 online resource (1 video file, approximately 6 hr., 13 min.)
Issuing body
Made available through: O'Reilly Media Company.
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Reproduction note
Electronic reproduction.
Specific material designation
remote
System details
Mode of access: World Wide Web

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