Adam Optimizer Explained in Detail | Deep Learning

Adam Optimizer Explained in Detail. Adam Optimizer is a technique that reduces the time taken to train a model in Deep Learning.
The path of learning in mini-batch gradient descent is zig-zag, and not straight. Thus, some time gets wasted in moving in a zig-zag direction. Adam Optimizer increases the horizontal movement and reduced the vertical movement, thus making the zig-zag path straighter, and thus reducing the time taken to train the model.
Adam Optimizer is formed by the combination of two Optimizers in Deep Learning, which are Momentum Optimizer and RMSprop Optimizer.
Thus Adam Optimizer is the most powerful optimizer in Deep Learning.
The concept of Adam Optimizer is difficult to understand. Thus in this video, I have done my best to provide you with a detailed Explanation of the Adam Optimizer.
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▶ Momentum Optimizer in Deep Learning: • Momentum Optimizer in ...
▶ RMSprop Optimizer in Deep Learning: • RMSprop Optimizer Expl...
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✔ Complete Linear Regression Playlist: www.youtube.com/watch?v=mlk0r...
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Timestamp:
0:00 Agenda
1:52 Adam Optimizer Explained
4:35 End
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[@mariap.9768]

You are much more clear and concise than other similar videos.

[@nbiresev]

Great explanation, thanks a lot. I watched first your video where you explained all optimization which was a bit confusing, but after watching each of them individually it became clear.

[@sirborkington1052]

Thanks mate, helped a lot.

[@GK-jw8bn]

in this video you havent mentioned that adam allows to learn adaptive rates for each individual parameter

[@user-yl2zt1ol4n]

Very great explanation! I needed a clear overview which concepts are needed or from where they arise. I need to test different first order optimization methods for my master thesis for a special multidimensional optimization problem for a bioinformatics project. Recent papers are nice, but don´t visualize or explain it short and simple. Thanks alot!

[@CodingLane]

Glad to help !

[@mathid_]

What is the value of Vdw and Sdw?

[@niloydey6147]

don’t you have to calculate bias corrected estimates?

[@CrashBandicoot-qp8vq]

can you please reference the values of beta1 and beta2 and epsilon ?

[@kumruorkun3947]

there is a one thing i cant get it. İn RMSprop why we divide dw or db to square root of sdw plus epsilon? Can anyone explain?

[@nbiresev]

Epsilon is added in order to avoid dividing by value that is zero (or very close to zero as then the whole term is huge).MY understanding for division by square root of mean square of dW is that it adapts weight update to the most recent training samples.

[@jordiwang]

good videooooo broooo, straight to the point

[@shahomaghdid9591]

Thank you so much!

[@sannidhyamaheshwari4772]

best + precise + clear = amazing

[@pranaysingh3950]

I am done with all the optimizers finally. Thanks a ton.

[@CodingLane]

Your welcome!

[@pranaysingh3950]

@@CodingLane Yea but bro ? the doubt ... okay that's fine. No problem.

[@CodingLane]

Hi @@pranaysingh3950 , I don’t see your doubt posted. Where did you ask? Can you please tag the message/comment ?

[@AbhishekVerma-kj9hd]

Kya smjhate ho bhai maza aa gaya

[@ahmadjohara7824]

Nice job! thanks alot.

[@CodingLane]

Welcome!

[@fagunraythatha5601]

Nicely explained brother

[@lochuynh6734]

Great explaination, great video

[@CodingLane]

Thank you so much! I highly appreciate your support!

[@parthpatwari3174]

thank you

[@tanvirtanvir6435]

0:56 2 algorithm

[@varkam1523]

Rajesh kanna yaha se photo uthaya

[@brianp9054]

worth noting that you said nothing

[@moonedCake]

Thanks a lot! 🤍

[@CodingLane]

Welcome 😇