Answers.Body: <p>Welcome to AI.</p>
  <p>Let me answer your question with a question: what do you want to do with Deep Learning?</p>
  <p>If you want to use it on the real world, then science is critical.  Science has two technical elements: statistics (seeing through the fog) and Differential Equations (gearbox of the universe).  Many folks in the field have neither, so having a little can go a good way.  The best way to get good at doing is to do, so practicing the problems, especially applied to something where you have skin in the game and being wrong hurts (but doesn't wreck you) and you can tell when you are wrong, is going to help with learning.  Try to &quot;<a href="http://www.pz.harvard.edu/resources/making-learning-whole-how-seven-principles-of-teaching-can-transform-education" rel="nofollow noreferrer">make learning whole</a>&quot; for yourself.</p>
  <p>If you want to use it for business, then you need business-related expertise, and the most important one of those is to not deceive yourself.  As Hofstadter said &quot;you are the easiest person for you to deceive&quot; so that takes work.  The second most important is being able to relate the work to dollar bills, to &quot;show the money&quot;.</p>
  <p>If you want something capable for silicon or medicine, then you need to know MCA and get some two-handed iron-fisted grip on sample size, sources of variation, and the NIST principles driving &quot;process capability analysis&quot;.</p>
  <p>For art, or for 'the math of it', I'm probably not able to provide solid answers there.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2023-01-24T15:04:57.283Z
Answers.LastActivityDate: 2023-01-24T15:04:57.283Z
Answers.UserDisplayName: EngrStudent
Body: <p>Recently I've been wondering about the necessary amount of math that a deep learning scientist really needs to know. From what I could gather around the internet there are 3 big areas(calculus 1,2 and, linear algebra and statistics) that every developer should know, but are topics like Real Analysis, Topology and PDEs also useful for this field or are they of barely used?</p>
  
Tags: <deep-learning><math>
Title: How much pure math should I know for deep learning?
UserDisplayName: Tom PL
ThreadText: # How much pure math should I know for deep learning?
  
  (Asked by: username_0 on 2023-01-24)
  
  Recently I've been wondering about the necessary amount of math that a deep learning scientist really needs to know. From what I could gather around the internet there are 3 big areas(calculus 1,2 and, linear algebra and statistics) that every developer should know, but are topics like Real Analysis, Topology and PDEs also useful for this field or are they of barely used?
  
  **Tags:** deep-learning, math
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2023-01-24. Score: 0
  
  Welcome to AI.
  
  Let me answer your question with a question: what do you want to do with Deep Learning?
  
  If you want to use it on the real world, then science is critical. Science has two technical elements: statistics (seeing through the fog) and Differential Equations (gearbox of the universe). Many folks in the field have neither, so having a little can go a good way. The best way to get good at doing is to do, so practicing the problems, especially applied to something where you have skin in the game and being wrong hurts (but doesn't wreck you) and you can tell when you are wrong, is going to help with learning. Try to "[make learning whole](http://www.pz.harvard.edu/resources/making-learning-whole-how-seven-principles-of-teaching-can-transform-education)" for yourself.
  
  If you want to use it for business, then you need business-related expertise, and the most important one of those is to not deceive yourself. As Hofstadter said "you are the easiest person for you to deceive" so that takes work. The second most important is being able to relate the work to dollar bills, to "show the money".
  
  If you want something capable for silicon or medicine, then you need to know MCA and get some two-handed iron-fisted grip on sample size, sources of variation, and the NIST principles driving "process capability analysis".
  
  For art, or for 'the math of it', I'm probably not able to provide solid answers there.
  
  
  ---
  
  