The Resource Calculus for Machine Learning LiveLessons, Krohn, Jon
Calculus for Machine Learning LiveLessons, Krohn, Jon
Resource Information
The item Calculus for Machine Learning LiveLessons, Krohn, Jon represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in Merrimack Valley Library Consortium.This item is available to borrow from 1 library branch.
Resource Information
The item Calculus for Machine Learning LiveLessons, Krohn, Jon represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in Merrimack Valley Library Consortium.
This item is available to borrow from 1 library branch.
 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 calculusthe study of rates of changefrom 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 singlevariable derivative calculus to detail gradients of learning (which are facilitated by partialderivative 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 partialderivative, 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 highlevel software libraries (e.g., scikitlearn, 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..
 Language

 eng
 eng
 Edition
 1st edition
 Extent
 1 online resource (1 video file, approximately 6 hr., 13 min.)
 Label
 Calculus for Machine Learning LiveLessons
 Title
 Calculus for Machine Learning LiveLessons
 Statement of responsibility
 Krohn, Jon
 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 calculusthe study of rates of changefrom 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 singlevariable derivative calculus to detail gradients of learning (which are facilitated by partialderivative 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 partialderivative, 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 highlevel software libraries (e.g., scikitlearn, 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
 Carrier category
 online resource
 Carrier category code

 cr
 Carrier MARC source
 rdacarrier
 Color
 multicolored
 Content category
 twodimensional 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
 Carrier category
 online resource
 Carrier category code

 cr
 Carrier MARC source
 rdacarrier
 Color
 multicolored
 Content category
 twodimensional 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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<div class="citation" vocab="http://schema.org/"><i class="fa faexternallinksquare fafw"></i> Data from <span resource="http://link.mvlc.org/portal/CalculusforMachineLearningLiveLessonsKrohn/dS1YDe5azyI/" typeof="Book http://bibfra.me/vocab/lite/Item"><span property="name http://bibfra.me/vocab/lite/label"><a href="http://link.mvlc.org/portal/CalculusforMachineLearningLiveLessonsKrohn/dS1YDe5azyI/">Calculus for Machine Learning LiveLessons, Krohn, Jon</a></span>  <span property="potentialAction" typeOf="OrganizeAction"><span property="agent" typeof="LibrarySystem http://library.link/vocab/LibrarySystem" resource="http://link.mvlc.org/"><span property="name http://bibfra.me/vocab/lite/label"><a property="url" href="http://link.mvlc.org/">Merrimack Valley Library Consortium</a></span></span></span></span></div>