Tutorial 12- Stochastic Gradient Descent vs Gradient Descent
ฝัง
- เผยแพร่เมื่อ 27 ก.ค. 2019
- Below are the various playlist created on ML,Data Science and Deep Learning. Please subscribe and support the channel. Happy Learning!
Deep Learning Playlist: • Tutorial 1- Introducti...
Data Science Projects playlist: • Generative Adversarial...
NLP playlist: • Natural Language Proce...
Statistics Playlist: • Population vs Sample i...
Feature Engineering playlist: • Feature Engineering in...
Computer Vision playlist: • OpenCV Installation | ...
Data Science Interview Question playlist: • Complete Life Cycle of...
You can buy my book on Finance with Machine Learning and Deep Learning from the below url
amazon url: www.amazon.in/Hands-Python-Fi...
🙏🙏🙏🙏🙏🙏🙏🙏
YOU JUST NEED TO DO
3 THINGS to support my channel
LIKE
SHARE
&
SUBSCRIBE
TO MY TH-cam CHANNEL
Amazing explanation Sir! You'll always be the hero for the AI Enthusiasts. Thanks a lot!
what an amazing teacher you are. Crystal clear.
whenever i am confused with some topics , i come back to this channel and watch your videos and it helps me a lot sir .Thank you sir for an amazing explanation
Best Deep Learning playlist on youtube
I saw many videos but this one is quite comprehensible and informative
This is excellent explanation so that anyone can understand with so much granular level of details.
Excellent explanation, it was really helpful thank you.
This video is such a light bulb moment for me :D Thank you so very much!!
Krish sir your youtube channel is just like GITA for me as one gets all the answers to life in GITA I get all my doubts cleared on your channel.
Thank you, SIr.
after becoming member how can i get the data science material, can you please tell me?
Superb...simply superb. understood the concept now from the Loss function. Well don Krish.
My God! Finally am clear about GD SGD and mini batch SGD!
Great explanation . Very clear . Thank!
Your vidoes are excellent reference to brush up these concepts
What an explaination 🧡 . Great !! Awesome !! .
Explained very well. Thankyou.
Really this is the best video i'v seen ever explaining the concept better than famous. school
Thank you, It is clear explanation. I got it!
such a clean way of explanation
Thanks Krish !!! very nice explanation
Awesome @KrishNaik Sir.
How are you so good at explaining 😭😭😭😭😭 Thanks a lot ♥♥♥
Krish you concise subject most meaningfully
Great video man 👍👍..Please keep it up. I am waiting for next videos
Thanks Krish. Good video.I want to use all this knowledge in my next batch of deep learning by ineuron
thanks for this! great explanation
Hi Krish, one request to you ...like this playlist, please make long videos for the ML Playlist with the Loss Functions , Optimizers used in various ML Algorithms --> mainly in case of Classification Algorithms
Good Good clearly explained nobody can explained like this
Excellent explanation Sir!
Top notch explanation!
Hi, is it completely theoretical or will you code in further sessions?
This is what i was looking for
Excellent!
greate video excelent effort. appreciated!!
Great one!
yes i really liked this explanation thanks
good explanation! thankss
Great guy.
If we use a sample of output to find the loss, will we use its derivative for changing whole weight or change the weights of the respective output
Sir your videos are amazing. Can you please explain about latest methodologies such as BERT , ELMO
You are AWESOME! :)
Perfect ..!!!
Just wanted to ask to ask if you could also suggest some good resources online that we can read which could bring more clarity.......
Thank you, sir!
Good Explanation! But you did not speak much about when to use SGD although you clarified better on GD and Mini Batch SGD
There is nothing much to explain about SGD when you are talking about 1 datapoint at a time while considering dataset of 1000 datapoints.
great video !!
Good attempt 👍. Please record with camera on manual focus.
the only video that explains
You are a HERO sir
very good explanation
have you make the videos of practical implementation of all the work if so please share the links
Thank you sir.
Great Sir
thank you very much for your efforts. please how can we solve a portfolio allocation problem using this algorithm? please answer me
hats off man
thanks Krish
1000 likes for you man👏👍
Thanks buddy
I have 1 question regarding this topic. Is this concept applicable to linear regression, right?
which have more convergence speed SGD or GD ?
Nice bro
How can we take k inputs at the same time
Sir, please solve my problem, in my view we are doing gradient descent to find the best value of m (slop in case of linear regression, considering b = 0) so if we use all the point then we must came to know at which point the value of m is less, so why we have to use learning rate to update weight because we already know the best value.
Thanks
Thank u sir 🙏🙏🙌🧠🐈
can you share the paper for reference and also can you share the resources for deep learning for image processing.
9.28 time, you said sgd will take time to converge than gd, then which is fast , sgd or gd????
Hello Sir, could you share the link for the code where you explained, these videos series are very nice with short of the period we can cover so many concepts. :)
when you mentioned SGD takes place in linear regression . I didnt understand that comment . Even in your linear regression videos for the mean square error we are having sum of squares for all data points . So how SGD got linked in linear regression ?
please do tell about stochastic gradient ascent also
what is resource of data point?
8:58 , using GD it converge quickly and while using mini-batch SGD it follows zigzag path, How??
In case of mini batch sgd, we are considering only some points so some deviations will be there in the calculation compared to usual gradient descent where we are considering all values. Simple example GD is like total population and mini SGD is like sample population, it will never be equal and in sample population some deviation always will be there in distribution compared to total population distribution.
We cant use GD everywhere, due to time computation factor, using mini SGD will give approximate correct result.
@@kannanparthipan7907 Deviation will be there in the final output or in the final converge result. Question is why do we have during the process of convergence. Also for every epoch if we consider different samples then understood that there can be zig zag results in the process of convergence. But if only one sample of k records are considered then why is that zig zag during convergence?
Ok now I got it. For every iteration, samples are picked at random, so is zig zag. Just gone through other artciles
py:28: RuntimeWarning: overflow encountered in scalar power
cost = (1/n)*sum([value**2 for value in(y-y_predicted)]) hey bro . ia m stuck here with this error , i could not understand the error itself, if you suggests me some solution. .... just now i started to practice a ml algorthm.
good video
@12:02 Sir it should bemini batch stocastic g.d.
please what the difference between GD and Batch GD !
sir can you explain me SPGD algorithm please
please workout your camera issue it seems like it is set to auto focus resulting in a little disturbance.
negative weights and positive weights best explained as--
since the angle of tangent is more than 90 degree in left side of the curve so this results in -ve values and for other its less than 90 degree so it would be +ve
4:17 SGD have minimum 256 records to find error / minima you said it's 1 record at a time
I read few articles which says In "SGD a randomly one data point is picked from the whole data set at each iteration". 256 records which you're talking about may be Mini Batch SGD "It is also common to sample a small number of data points instead of just one point at each step and that is called “mini-batch” gradient descent."
@@pramodyadav4422 yeah ,even I have read that in SCD only one data point is selected and updated in each iteration instead of all.
Confusing one !
Switch the auto focus feature in your camera. It is distracting.
our videos are good but camara was bad
Why dont you buy him a new one ?
Pora eri poka
do change your method of teaching seems like someone has read a book and just trying to copy thatt content from ones side .....use your own ideologies for it
:)