Answers.Body: <p>Bear in mind the context is surrounding this video with the rest of Andrew Ng's work. So please watch that to get a better background.
  &quot; In the beginning there is Time. Time moves, and creates dynamics on Space.&quot;</p>
  <ul>
  <li><a href="https://www.youtube.com/watch?v=yFPLyDwVifc" rel="nofollow noreferrer">1.2.5 Gradient Descent by Andrew
  Ng</a></li>
  <li><a href="http://awibisono.github.io/2016/06/13/gradient-flow-gradient-descent.html" rel="nofollow noreferrer">Gradient descent is in discrete time, gradient flow is in continuous
  time</a></li>
  </ul>
  <p><strong>Links</strong></p>
  <ol>
  <li><a href="https://www.youtube.com/watch?v=yFPLyDwVifc" rel="nofollow noreferrer">https://www.youtube.com/watch?v=yFPLyDwVifc</a></li>
  <li><a href="http://awibisono.github.io/2016/06/13/gradient-flow-gradient-descent.html" rel="nofollow noreferrer">http://awibisono.github.io/2016/06/13/gradient-flow-gradient-descent.html</a></li>
  <li><a href="https://www.youtube.com/results?search_query=gradient+descent+and+gradient+flow" rel="nofollow noreferrer">https://www.youtube.com/results?search_query=gradient+descent+and+gradient+flow</a></li>
  </ol>
  <p>First time answering on this site so please comment and I will fix what I can.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2023-06-06T07:29:48.093Z
Answers.LastActivityDate: 2023-06-06T07:38:39.493Z
Answers.LastEditDate: 2023-06-06T07:38:39.493Z
Answers.UserDisplayName: lloyd
Answers.Body: <p>I highly recommend the Machine Learning Crash Course for understanding Gradient Descent. Yes, of course, this course is primarily aimed at an introduction to Tensorflow, but the initial lessons will help you in studying your question.
  <a href="https://developers.google.com/machine-learning/crash-course" rel="nofollow noreferrer">https://developers.google.com/machine-learning/crash-course</a></p>
  <p>I would also recommend this incredibly cool article for study. This is the best I've come across on this subject, but it will take you some time to figure it out. After studying it (and a number of others), I finally managed to write my first vanilla neural network:
  <a href="https://mattmazur.com/2015/03/17/a-step-by-step-backpropagation-example/" rel="nofollow noreferrer">https://mattmazur.com/2015/03/17/a-step-by-step-backpropagation-example/</a></p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2023-06-06T12:48:43.307Z
Answers.LastActivityDate: 2023-06-06T12:48:43.307Z
Answers.UserDisplayName: kvadrat
Body: <p>Understanding the concept of &quot;Gradient Flow&quot; can be quite difficult as there is a lack of widely recognized and clearly defined resources that provide a comprehensive explanation. Although you can find insights from machine learning experts and references to papers that mention gradient flow, there isn't a single, definitive source that thoroughly covers the topic.</p>
  <p>Could you please recommend a resource that offers a detailed overview of the gradient flow?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2023-05-27T19:14:19.803Z
Comments.Text: Please share the insights that you have seen. This provides context for others trying to help you out!
Comments.UserDisplayName: Robin van Hoorn
Tags: <deep-learning><reference-request><backpropagation><gradient-descent>
Title: Is there a resource that offers a detailed overview of the gradient flow?
UserDisplayName: v1998199904
ThreadText: # Is there a resource that offers a detailed overview of the gradient flow?
  
  (Asked by: username_0 on 2023-05-27)
  
  Understanding the concept of "Gradient Flow" can be quite difficult as there is a lack of widely recognized and clearly defined resources that provide a comprehensive explanation. Although you can find insights from machine learning experts and references to papers that mention gradient flow, there isn't a single, definitive source that thoroughly covers the topic.
  
  Could you please recommend a resource that offers a detailed overview of the gradient flow?
  
  **Tags:** deep-learning, reference-request, backpropagation, gradient-descent
  
  > **Comments:**
  > **username_1**: Please share the insights that you have seen. This provides context for others trying to help you out!  
  
  ---
  
  ## 2 answers
  
  ### Answer by username_2 on 2023-06-06. Score: 1
  
  Bear in mind the context is surrounding this video with the rest of Andrew Ng's work. So please watch that to get a better background.
  " In the beginning there is Time. Time moves, and creates dynamics on Space."
  
  * [1.2.5 Gradient Descent by Andrew
    Ng](https://www.youtube.com/watch?v=yFPLyDwVifc)
  * [Gradient descent is in discrete time, gradient flow is in continuous
    time](http://awibisono.github.io/2016/06/13/gradient-flow-gradient-descent.html)
  
  **Links**
  
  1. <https://www.youtube.com/watch?v=yFPLyDwVifc>
  2. <http://awibisono.github.io/2016/06/13/gradient-flow-gradient-descent.html>
  3. <https://www.youtube.com/results?search_query=gradient+descent+and+gradient+flow>
  
  First time answering on this site so please comment and I will fix what I can.
  
  
  ---
  
  ### Answer by username_3 on 2023-06-06. Score: 0
  
  I highly recommend the Machine Learning Crash Course for understanding Gradient Descent. Yes, of course, this course is primarily aimed at an introduction to Tensorflow, but the initial lessons will help you in studying your question.
  <https://developers.google.com/machine-learning/crash-course>
  
  I would also recommend this incredibly cool article for study. This is the best I've come across on this subject, but it will take you some time to figure it out. After studying it (and a number of others), I finally managed to write my first vanilla neural network:
  <https://mattmazur.com/2015/03/17/a-step-by-step-backpropagation-example/>
  
  
  ---
  
  