• LM101-083: Ch5: How to Use Calculus to Design Learning Machines

  • 2020/08/29
  • 再生時間： 34 分
  • ポッドキャスト

LM101-083: Ch5: How to Use Calculus to Design Learning Machines

無料で聴く

ポッドキャストの詳細を見る

  • サマリー

  • This particular podcast covers the material from Chapter 5 of my new book “Statistical Machine Learning: A unified framework” which is now available! The book chapter shows how matrix calculus is very useful for the analysis and design of both linear and nonlinear learning machines with lots of examples. We discuss how to use the matrix chain rule for deriving deep learning descent algorithms and how it is relevant to software implementations of deep learning algorithms.  We also discuss how matrix Taylor series expansions are relevant to machine learning algorithm design and the analysis of generalization performance!!

    For additional details check out: www.learningmachines101.com and www.statisticalmachinelearning.com

     
    続きを読む 一部表示
activate_samplebutton_t1

あらすじ・解説

This particular podcast covers the material from Chapter 5 of my new book “Statistical Machine Learning: A unified framework” which is now available! The book chapter shows how matrix calculus is very useful for the analysis and design of both linear and nonlinear learning machines with lots of examples. We discuss how to use the matrix chain rule for deriving deep learning descent algorithms and how it is relevant to software implementations of deep learning algorithms.  We also discuss how matrix Taylor series expansions are relevant to machine learning algorithm design and the analysis of generalization performance!!

For additional details check out: www.learningmachines101.com and www.statisticalmachinelearning.com

 
続きを読む 一部表示

LM101-083: Ch5: How to Use Calculus to Design Learning Machinesに寄せられたリスナーの声

カスタマーレビュー：以下のタブを選択することで、他のサイトのレビューをご覧になれます。

Audible.co.jp

Amazon.co.jp
レビューはまだありません。