Vector Norms - Explained

Understand vector norms in linear algebra with this clear explanation of L1 norm (Manhattan distance), L2 norm (Euclidean distance), and L-infinity norm. Learn how to measure vector size, explore the p-norm formula, and see how different norms shape geometry and impact machine learning regularization (L1 vs L2). Perfect for beginners in math, data science, and machine learning. *Related Videos* ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ K-Means Clustering: https://youtu.be/dyG9cj5RKL0 Support Vector Machines: https://youtu.be/K1EcCjDD_q4 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 *Follow Me* ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ 🐦 X: @datamlistic https://x.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 #linearalgebra #machinelearning #datascience