Name two short comings of basic clustering and embeddings such as K-means and PCA that lead to wanting DNN.

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1. Name two short comings of basic clustering and embeddings such as K-means and PCA that lead to wanting DNN.

1) Objects in background of images not taken into account. 2) Translations and deformations are very bad on results.
A representation meaningful at a certain location can / should be used everywhere. It applies the same linear transformation locally, everywhere, and preserves the signal structure.
1) Look at the parameters: 2) Look at the activations: 3) Look at how the network behaves 'around the image'; with Occlusion sensitivity, saliency maps, GradCam
Depth shatters the relation between the input and the gradient wrt the input. Residual networks limit this effect. Since linear networks avoid this problem, it has been suggested to use Looks Linear initialization, which makes the network linear initially.

Name two short comings of basic clustering and embeddings such as K-means and PCA that lead to wanting DNN.

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Name two short comings of basic clustering and embeddings such as K-means and PCA that lead to wanting DNN.

❤ If you liked Fatskills, consider supporting us by checking out The Life Manuals You Never Got.

About | Explore | User Guide | Topics | Subjects | Doubt Solver | Career Aptitude Test | Answers | Free Tools | What Should We Know? Privacy | Terms |

Without work one finishes nothing. - Ralph Waldo Emerson© 2026 Fatskills.com

All trademarks, logos and brand names are the property of their respective owners. All company, product and service names used in this website are for identification purposes only. Use of these names, trademarks and brands does not imply endorsement.

About | Explore | User Guide | Topics | Subjects | Doubt Solver | Career Aptitude Test | Answers | Free Tools | What Should We Know?
Privacy | Terms |

Without work one finishes nothing. - Ralph Waldo Emerson
© 2026 Fatskills.com