Bias and Variance for Machine Learning | Deep Learning

[@AlexKashie]

Bias = Assumptions ---> Underfitting Variance = Sensitivity ---> Overfitting You made it crystal clear, thank you.

[@roaklarson9699]

Explained a boring 30+ minute lecture to an easy, consumable less than 10 minute video. Well done!

[@gaspad_]

Extraordinary explanation, thank you!

[@unknownhero6187]

Excellent description of a bias and variance! Impatiently waiting for a new video!

[@AssemblyAI]

Great to hear!

[@RealMatinMahmoudi]

the best video about the subject on the internet.

[@AssemblyAI]

Great to hear, thank you!

[@dhairyachhabra8685]

Great Explanation

[@Ah3laly]

That's Awesome, Thanks (y)

[@rpapa]

Great Explanation !!!!!!!Thank you 😁

[@anirbansarkar6306]

one of the best video out there comparing bias and variance. Thanks Assembly AI

[@AssemblyAI]

Glad you liked it!

[@narendrapratapsinghparmar91]

THanks for your efforts.

[@KhoulaAbdullah-zm4lx]

Thank you so much for such an amazing explanation!

[@Viclevicking]

excellent video

[@AssemblyAI]

Thank you!

[@asdfafafdasfasdfs]

What is the difference between training the model more and introduce more data?

[@emadbagheri2755]

Great

[@ashishpatil9774]

When someone this cute teaches you can't help but give your 100% attention 😂

[@elmoudir1170]

thanks.. u are so cute❤️❤️❤️❤️❤️