Difference between revisions of "Step 3"

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# Also, [https://forms.gle/v6fEGLRWkXJ6UZXi9 Step 2 homework] reminder
 
# Also, [https://forms.gle/v6fEGLRWkXJ6UZXi9 Step 2 homework] reminder
 
# Refresh in your memory the gradient-based optimization, see either:
 
# Refresh in your memory the gradient-based optimization, see either:
## [https://en.wikipedia.org/wiki/Gradient_descent Gradient descent [https://en.wikipedia.org/wiki/Conjugate_gradient_method Conjugate gradient],
+
## [https://en.wikipedia.org/wiki/Gradient_descent Gradient descent],  [https://en.wikipedia.org/wiki/Conjugate_gradient_method Conjugate gradient descent], [https://en.wikipedia.org/wiki/Gradient Gradient], [https://en.wikipedia.org/wiki/Directional_derivative Directional derivative], [https://en.wikipedia.org/wiki/Jacobian_matrix_and_determinant Jacobian matrix], [https://en.wikipedia.org/wiki/Hessian_matrix Hessian matrix], [https://en.wikipedia.org/wiki/Vector_field#Gradient_field_in_Euclidean_spaces Gradient field], or
[https://en.wikipedia.org/wiki/Gradient Gradient],
 
[https://en.wikipedia.org/wiki/Directional_derivative Directional derivative],
 
[https://en.wikipedia.org/wiki/Jacobian_matrix_and_determinant Jacobian matrix],
 
[https://en.wikipedia.org/wiki/Hessian_matrix Hessian matrix],
 
[https://en.wikipedia.org/wiki/Vector_field#Gradient_field_in_Euclidean_spaces Gradient field], or
 
 
## the book
 
## the book
  

Revision as of 13:36, 29 September 2024

We are moving towards the coursework. How do you put your project description in one pitch? We reformulate the deliveries from Step 1 and Step 2 in an informal problem statement.

The seminar

  1. The warm-up 5-minute test
  2. Tensor product and tensor decomposition
  3. The first slide
  4. Homework discussion

Resources

Step 3 YouTube video (expected with online version)

Homework

  1. For your selected project write the motivational part using this template for Step 3.
  2. Download the link to your homework here.
  3. Also, Step 2 homework reminder
  4. Refresh in your memory the gradient-based optimization, see either:
    1. Gradient descent, Conjugate gradient descent, Gradient, Directional derivative, Jacobian matrix, Hessian matrix, Gradient field, or
    2. the book


Transcript of the video

Appears after the seminar.