134 - What are Optimizers in deep learning? (Keras & TensorFlow)

[andreeamada]

Thank you for your video! Love the analogies with the blind folded hiker and the ball, really makes sense to me now!

[zeshn]

Very good video. learned the functioning of optimisers in just 8 minutes.

[rshaikh]

Thank you for explaining the concepts so clearly.

[gvomrtt]

You are the BEST teacher. Thank you!!! All the best for you sir Sreeni.

[blaisofotso]

Hallo There, so like the way you explained tthe concept

[himalayakilaru]

Excellent Explanation. Thank you so much. One question however. So you are saying when I use Adam optimizer I dont have to explicitly define the learning rate right? but what happens when I do - optimizerr = . Now what does that mean? My understanding is that the Adam optimizer starts with a learning rate of 5e-5 and it will take it from there? Is that so ? TIA.

[manuelpopp]

Hi Sreeni, thanks for the video. Regarding the default values, in the TensorFlow description of Adam, they wrote "The default value of 1e-7 for epsilon might not be a good default in general. For example, when training an Inception network on ImageNet a current good choice is 1.0 or 0.1". Does it make sense to test several values here?
Also, I wondered whether it makes sense at all to pass a learning rate schedule to Adam?

[MohammedHAzeez]

Is it possible, please, to attach for us a link to the research paper that talks about Adam optimizes?

[mychanneltest]

or when we talk about optimisation, are we talking about finding the best parameters? E.g. similar to how it's done with hyperparameter tuning for RF, DT, etc...

[nirmalanarisetty]

Hello sir, it is very much informative for beginners. if possible make tutorial on stacked denoising autoencoder for intrusion detection also positively

[minhaoling]

Hi sir, could you upload slides for all videos you posted ?

[wiczus]

2:25 Doesn't TF transform the equations used for the input into the respective derivative? It's mathematically different from probing 2 points.

[mychanneltest]

Hi Sreeni, I am a beginner with python, just learning the hooks. I thought every ML model tries to reduce the error anyway (e.g. linear regression by fitting the line and reducing the residuals...) So, what do we need optimizers for then? I don't get it. Can anyone explain?

[samarafroz]

Sir please make tutorial on image processing and segmentation with deep learning.

[thebiggerpicture__]

Sorry, I don't understand the role of the optimizer. We know the whole objective function is derivable. I thought we are just moving in the opposite direction of this derivative. Why did you say that the optimizer keep testing directions? Thanks!