Those were some of the best explanations about hidden layers and numbers of neurons I could find, also making it very easy to see in your python plots. Keep up the good work!

You are an amazing teacher, I hope this comment reaches you well! This is some top class free content! Thank you!

16:36 is the answer for how many layers do you need

Finally, someone who speaks math. Thank you sir

The approach to explaining it trough linear regression was very useful for me, thank you!

Such a wonderful explanation to the really fundamental question. I wonder why there is so little accessible information for beginners on this topic. Thanks a lot for the video.

Finally, an explanation that goes straight into code. Awesome!

I really loved your approach. You are explaining the technicalities and discussing the various possibilites while staying on the subject. It's thorough. With other youtubers, I felt like they were too basic and missing the crucial implementation part. Thankyou!

This is a fantastic explanation. I really appreciate how you involved the math as you walked through your implementation. A lot of people hand wave the math.

This is such an intuitive and helpful video. I can see that there is a lot of hard work behind this video. Great job!

I don't know why there are too few likes on such an awesome video..you are really great sir.

Strange isnt it, so many people asking THIS question, and so few people can answer it, THANK YOU

22:55 Rule 3 contradiction with rule 2 : input = 50, output = 50 . rule 2 gives hidden

Thanks for giving clarity on such an important notion. worth it.

great video. inlike ecuations.

Great video sir

Thank you for this decent explanation

Amazing..helps me lot for my research work. Thanks

this video is like finding gold ... thannnk youuu

Awesome video that is what i was looking for.

thanks much sir found right content after long search

Fantastic explanation.

many good hints and insights here

You are awesome .. you taught me this topic like pro

Thanks a lot. I regularly watch your Videos.

Great video and well explained!!

Powerful explination

Excellent video!

Hi Sreeni, I had a significant mistake and training and test data differences. This, in my opinion, is due to the huge values of the output response numbers, which have increased from 64 to over a thousand. Please, how can I resolve this issue? Can I divide them by their maximum value to fix the issue? What do you prefer, please?

wow, thank you so much for the great video. Sir can you make videos on segmentation using GAN and UNet ??

thanks sir, this is such an enlightenment 😂 ive been using 4 or even 6 layers by thinking that the model could learn very deep, like some unrecognized patterns 🤣🤣 but turns out just use 1 to 2 😭😭 thanks sir, im new to your channel this week btw 🙏

Least Squares Optimizer is same as Analytical Solution.(Wrote this comment to avoid confusion :) )

adding hidden layers without activation functions is essentially linear regression. If the problem is linearly separable, you can find a solution. complex problems with non-linear solutions require hidden layers with activation functions. A more complex solution requires a higher number of hidden layers and activation functions. The "magic" is in the activation function.

"If your problem can be solved with linear fitting..." Me: trying to survive 2020 ...

Thanks for the video very helpful

ok so my doubt is i read on stack exchange and also ur 3 rd point in node section that neuron size should be 2/3 of input size so here the input size is equals to number of unique features or length of features input (len of dataset) and also 2/3 neuron = all the neurons in all the layers or only in single layer

17:53 we can use dropout technique to reduce overfitting btw

Thank you, great explanation.

Many thanks

you are worth listening

Thank you sir, very nice explanation

excellent vedio that give me great help!think you sir~

Thanks!

error again when calculating mean squared error ! line 71 you should use: np.mean((y_test--pred)**2) not np.mean(y_test-y_pred)**2 !! Thank you for good content

Hi, thank you, may I know which tool is used to make this video?

Could you provide the data source details but it is very small dataset with very limited parameter. But I appreciate your video for clear clarification of the concept.

please I need an explanation of how to increase the layer of deep belief network from three-layer to more than 6 and its advantages and disadvantage .

OMG thank you, I finallyu understand

How about neural network without hidden layer for classification?

I didn't understand what's happenning, when the number of hidden nodes increase. Does that also lead to overfitting?

For my thesis I am using weather data to predict future values using the CNN but for my loss and Val loss I get nan values? Do you know of a way I could fix this sir?

I have 7lakhs data.so can you suggest me how many neuron can i use for my neural network.. i am using curve fitting neural network.

Can't we use Keras Tunner to find the exact number of layers and neurons required in the network?

10:43 I think that if the learning rate is too small it could get 'stuck' in a local minima, isn't it?

the number of neurons in both hidden layer, should be the same?

TLDR; 1 or 2 hidden layers - or just guess because he doesnt know

سلام.Hi