136 understanding deep learning parameters batch size

[ai-video-stories]

So far the most clear and concise explaination

[justinnails]

Thanks for the great video. It is useful to see how batch size affects the model.

[Raulvic]

Grate Video! Thanks for the amazing playlist!
One comment about the batch size analysis:
usually we increase the learning rate with the same rate we increase the batch size! This seems to mitigate the convergency issue shown in your analysis.

[Mohan-social]

This is a great video - I am happy i found your channel. It is amazing.!

[microcosmos]

Thank you for the great content! As always, very helpful and interesting to watch

[jameshawkes]

I am a little confused on how the parameters (weights) are updated after the batch has been processed. If two different observations in the training set go through the same nodes in the network, it would seem that the contribution the first observation made to changes in the weights would be lost when the second observation pass through the weights since the weights are not changed until the batch is competed. I am obviously missing something, could someone point me in the right direction.

[saikrishnavadali]

Amazing explanation.... and Amazing demonstration...

[satinathdebnath]

Very informative and great video. I am able to learn it first after watching these videos. While explaining the batch size you mentioned that in 1 epoch the model covers all the samples in 94 iterations. I understand that in each batch operation the weight and biases are updated for those samples and then moved forward for next batch. If by 94 iterations all the samples are already visited then what is the use of 5000 epochs? Could you please explain that too. If someone knows the answer please welcome. Thanks once again for such wonderful videos. I am an Msc student and happily learning from this source.

[sarabhian]

I have 100milion rows dataset, I want to do preprocessing for NLP (like tokenization, rearranging, label encoding etc..) how should I approach this problem .. pls help me

[hritikdj]

Great explanation sir, Thanks for sharing knowledge :)

[luciendaunas]

thank you so much for the explanation and the striking demonstration!

[ShahidAli-bkqg]

Sir, I am still confuse in this. if we have 500 images and we want to set batch size=20 so 500/20= 25 samples in each batch and epoch size is 5 so each epoch 25 sample will be given to model as forward pass and update weights right ?. my question is after given 25 samples and what about next epoch same 25 samples are given or other 25 samples from dataset which were not shown to model ? please answer my question.

[lakeguy]

It seems to me that the optimal batch size is a function of how large the training dataset is. Using your example, you've chosen 32 as batch size with a dataset of 3000 rows. That means each batch is approximately .011% of the dataset. If your dataset was much larger, (for example: 1, 000, 000 rows), wouldn't that imply that you should choose a batch size of 11, 000? that assumes that 11, 000 rows fits within the system ram and gpu ram? Am I on the right track here? (great video!)

[frankgkilima]

Thank you. Good tutorial. Good topic, well prepared and excellently explained.

[keeprun-a]

Thank you very very very much, this is deliciously useful

[MrMikael]

I have seen this very often, that the batch size is 2 to some power (4, 16, 32, 64 etc). Any reason behind that? If you have say 3000 samples, why not use a divisible batch size, such as 50?

[a.k.zraselrahman]

Outstanding explanation !!! I want to know why we need 200 epoch as in each epoch all 1000 data is passing through the model.
Why only one epoch is not enough as each epoch use hole dataset ?

[asraajalilsaeed]

Can use this code with GAN ???
And xtrain what choice? Real or fake image?

[SimonZimmermann]

Why is your batch size the number two to the power of n?