smooth explanation! I have a question. In my understanding a model is adjusted on every iteration. Whe you say different weights are applied on each epoch, is it a secondary adjustment ?
Each epoch adjust the weights but to complete each epoch it has to read the entire training data in multiple iterations since amount of data is huge. with just partial data it doesn't make sense to update the weights. passing the entire dataset through a neural network is not enough. And we need to pass the full dataset multiple times to the same neural network. So, updating the weights with single pass or one epoch is not enough. One epoch leads to underfitting
A training step is also called Iteration. Epoch indicates the number of passes of the entire training dataset the machine learning algorithm has completed.
best explanation, thank you !
In 3 mins you covered 3 topics like a piece of cake. Thank Yiu!
Ma Sha Allah
Very effective method
Very helpful video
Simply Explained , Gr8
Great video!
i read multiple articles and watched video to get difference between epoch&batch this is by far the best explanation, Subscribing now.
Thank you! Machine Learning topics are often so dense, they almost always gloss over material like this without properly explaining. c:
Thank you! I no longer feel mad
Very well explained
Super helpful man, thank a lot! Love the activate windows
Great video man!
Nice Explanation.Sir
Thank You so much Best
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Thank you this helped me alot i love your content
To the point !!!!!
This is soooooooo great sir!, thanks!!!.
Straight to the point and very clear. thanks so much. Another subscriber!
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Thank you!
Damn in just 3 mins you made me understand all the 4 terms. Thanks really good for my last hour prep.
Thanks for the video. Brief and best video so far on these topics.
Seriously this is by far the best explanation! It is very simple and easy to understand! Thank you! Subscribing now!
thank you so much :)
Thanks so much
Very good explanation. Thanks so much!
thankx
super explanation
thanks for the concise explanation!
love your simple and easy understand example. thanks
Good
Nicely explained bro, thanks
Thanks
thanks... perfect explanation!
Thanks :)
Thanks !
Hey there, great video. May i know the references for the explanation in your video at 2:19 ?
Very good explanation and to the point, thanks
3:00 I think it should be 4 iterations,because one epoch is equal to one forward and backword pass.plz explain.
Does increasing epochs lead to overfitting?
Yup. Too few may result in an underfit model.
what is the intuition behind batch size being the hyper parameter? How will it influence the model ?
why mfr units produces products in batches ?? suggest you to watch this one more time
So helps compute more efficiently? Rather than entire set?
@@harshavardhan6368 u got it
@@reachDeepNeuron yeah, i understood it in the first go but my question is if will help the model to perform better in terms of accuracy ?
smooth explanation!
I have a question. In my understanding a model is adjusted on every iteration. Whe you say different weights are applied on each epoch, is it a secondary adjustment ?
Each epoch adjust the weights but to complete each epoch it has to read the entire training data in multiple iterations since amount of data is huge. with just partial data it doesn't make sense to update the weights. passing the entire dataset through a neural network is not enough. And we need to pass the full dataset multiple times to the same neural network. So, updating the weights with single pass or one epoch is not enough. One epoch leads to underfitting
So epoch meaning one step in gradient decent for the neural network?
A training step is also called Iteration. Epoch indicates the number of passes of the entire training dataset the machine learning algorithm has completed.
Love the video but I have one question:
What is the difference between an iteration and a step?
A training step is also called Iteration.
So an iteration is another word for batch?
Id say batch is the bag of content.. iteration is the cashiers registration of the bag.. say in a shop for example.
Thanks!