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Orthogonal Matrices - Explained
Orthogonal matrices play a key role in machine learning, from neural network initialization and gradient stability to PCA, SVD, and QR decomposition. In this video, you’ll learn what makes a matrix orthogonal, why it preserves lengths and angles, and how it ensures numerical stability in deep learning and dimensionality reduction. Perfect for anyone exploring linear algebra, neural networks, or data science fundamentals.
*Related Videos*
▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬
The Hessian Matrix: https://youtu.be/9tp1kULwU2w
The Jacobian Matrix: https://youtu.be/6FesMicc844
Bayesian Optimization: https://youtu.be/Kq6_kzlwSUQ
Hyperparameters Tuning: Grid Search vs Random Search: https://youtu.be/G-fXV-o9QV8
The Kernel Trick: https://youtu.be/N_RQj4OL1mg
Cross-Entropy - Explained: https://youtu.be/Fv98vtitmiA
Dropout - Explained: https://youtu.be/FDF_Q3_98GQ
Overfitting vs Underfitting: https://youtu.be/B9rhzg6_LLw
Why Models Overfit and Underfit - The Bias Variance Trade-off: https://youtu.be/5mbX6ITznHk
Least Squares vs Maximum Likelihood: https://youtu.be/WCP98USBZ0w
*Contents*
▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬
00:00 - Intro
00:35 - Definition
01:38 - Geometric intuition
02:48 - Properties
04:39 - ML applications
07:06 - Summary
07:50 - Outro
*Follow Me*
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#machinelearning #deeplearning #linearalgebra #orthogonalmatrix #pca
Видео Orthogonal Matrices - Explained канала DataMListic
*Related Videos*
▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬
The Hessian Matrix: https://youtu.be/9tp1kULwU2w
The Jacobian Matrix: https://youtu.be/6FesMicc844
Bayesian Optimization: https://youtu.be/Kq6_kzlwSUQ
Hyperparameters Tuning: Grid Search vs Random Search: https://youtu.be/G-fXV-o9QV8
The Kernel Trick: https://youtu.be/N_RQj4OL1mg
Cross-Entropy - Explained: https://youtu.be/Fv98vtitmiA
Dropout - Explained: https://youtu.be/FDF_Q3_98GQ
Overfitting vs Underfitting: https://youtu.be/B9rhzg6_LLw
Why Models Overfit and Underfit - The Bias Variance Trade-off: https://youtu.be/5mbX6ITznHk
Least Squares vs Maximum Likelihood: https://youtu.be/WCP98USBZ0w
*Contents*
▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬
00:00 - Intro
00:35 - Definition
01:38 - Geometric intuition
02:48 - Properties
04:39 - ML applications
07:06 - Summary
07:50 - Outro
*Follow Me*
▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬
🐦 Twitter: @datamlistic https://twitter.com/datamlistic
📸 Instagram: @datamlistic https://www.instagram.com/datamlistic
📱 TikTok: @datamlistic https://www.tiktok.com/@datamlistic
👔 Linkedin: https://www.linkedin.com/company/datamlistic
*Channel Support*
▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬
The best way to support the channel is to share the content. ;)
If you'd like to also support the channel financially, donating the price of a coffee is always warmly welcomed! (completely optional and voluntary)
► Patreon: https://www.patreon.com/datamlistic
► Bitcoin (BTC): 3C6Pkzyb5CjAUYrJxmpCaaNPVRgRVxxyTq
► Ethereum (ETH): 0x9Ac4eB94386C3e02b96599C05B7a8C71773c9281
► Cardano (ADA): addr1v95rfxlslfzkvd8sr3exkh7st4qmgj4ywf5zcaxgqgdyunsj5juw5
► Tether (USDT): 0xeC261d9b2EE4B6997a6a424067af165BAA4afE1a
#machinelearning #deeplearning #linearalgebra #orthogonalmatrix #pca
Видео Orthogonal Matrices - Explained канала DataMListic
orthogonal matrices machine learning deep learning neural networks linear algebra matrix decomposition qr decomposition pca svd dimensionality reduction orthogonal initialization ai data science gradient stability matrix math math for ml neural network training rotation matrices rigid transformations numerical stability ml basics orthogonal transformations
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29 октября 2025 г. 0:44:56
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