In this video we discuss why do we perform feature scaling (normalization/standardization) when training machine learning models, focusing on curve fitting algorithms with gradient descent.
*Related Videos*
▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬
Why neural networks can learn any function: https://youtu.be/O45AaRPQhuI
Why weight regularization reduces overfitting: https://youtu.be/Im91WYsbZ_U
*Contents*
▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬
00:00 - Intro
00:46 - Optimization function
01:13 - Learing rate variations
01:45 - Scaling variations
03:50 - Un/Normalize data
05:17 - Outro
*Follow Me*
▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬
🐦 Twitter: @datamlistic https://twitter.com/datamlistic
📸 Instagram: @datamlistic https://www.instagram.com/datamlistic
📱 TikTok: @datamlistic https://www.tiktok.com/@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
#features #standardization #normalization
