THIS IS LITERALLY INSANE. i understood this topic in half hour than 5 hours of uni class. I took uni class with so much so that i can understand this topic but i couldn't understand what the professor is talking about. You explain the concept in so simple way and an actuallly good example. Thank You.
That is awesome, simplified, practical, easy to understand, and a great tutorial. I subscribed to your channel and gave it a like. It would be very easy if you could run it side by side with coding.
best explanation so far! thank you. i have tried it in R using the neuralnet function with your dataset. even though i get the same coefficients with the log regression the weights and bias using the ANN are not the same. they are much lower. any idea why? =/
True, you can use just one output node when you predict just two categories. The R code I provided generates two output nodes but if you try TensorFlow in Python, it will use just one output if you set loss='binary_crossentropy'.
THIS IS LITERALLY INSANE. i understood this topic in half hour than 5 hours of uni class. I took uni class with so much so that i can understand this topic but i couldn't understand what the professor is talking about. You explain the concept in so simple way and an actuallly good example. Thank You.
You are Brilliant. i could not understand the whole concept until you explained in this video.
the best i have seen so far
This explanation is really complete and awesome. Thank you.
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Great content, thank you sir❤
Omg, I have tried to understand ANN without success until now. Thank you!
you are goat , best teacher ever
u r a wondeful tutor. God bless u
Just magistral 👏👏
Thank you, professor.
Thank you, looking forward to your next video about ANN
That is awesome, simplified, practical, easy to understand, and a great tutorial. I subscribed to your channel and gave it a like. It would be very easy if you could run it side by side with coding.
If you like to use Python, have a look at this video:
ua-cam.com/video/g0wuLPoBqhI/v-deo.html
love this
Wow... Great expectation as always 👍
best explanation so far! thank you. i have tried it in R using the neuralnet function with your dataset. even though i get the same coefficients with the log regression the weights and bias using the ANN are not the same. they are much lower. any idea why? =/
Did you use the exact same code as shown at 24:52?
jeeez. i have, but missed the threshold. that was it! many thanks!!!
Thank you!
Perfect!
can you please provide R code for finding values of b0 and b1
It is provided at 24:51 in the video.
How are the 2.747 and 5.7 derived?
That is explained at 11:30 and forward.
why using 2 output nodes? isn't P(healthy) equal to 1-P(cancer)?
True, you can use just one output node when you predict just two categories. The R code I provided generates two output nodes but if you try TensorFlow in Python, it will use just one output if you set loss='binary_crossentropy'.
@@tilestats
Thanks for the clarification
If you like this video, have a look at this playlist:
ua-cam.com/play/PLLTSM0eKjC2cSNGs87NyD3Q22t_qBEROY.html
for the first calculation, why u get -0.251?, i get -0.26
I think it is just due to rounding from previous steps.