How Gradient Descent Works. Simple Explanation

Video explain what is gradient descent and how gradient descent works with a simple example. Basic intuition and explanation are revealed in the video.The content is:
0:09 - What is Gradient Descent.
0:30 - Example
0:39 - Step no. 1. Start with a random point and find the gradient (derivative) of the given function.
1:28 - Step no. 2. Set learning rate to get know how big should be a step to move forward on gradient descent to the opposite direction.
1:47 - Step no. 3. Perform calculations on iterations.
1:58 - Initialize parameters.
2:20 - calculations on the 1st iteration.
3:20 - calculations on the 2nd iteration.
4:26 - till we reach global or local minimum.
I hope that this explanation on how gradient descent works is useful for beginners of deep learning or good reminder for machine learning / deep learning experts.
#deeplearning #gradientdescent #ai

[@blendaguedes]

Sometimes we just need two loops to understand a whole. Thank you!

[@uncoded0]

Thank you! Many hours of trying to understand gradient decent, now I finally get it, thanks to this video. Thank you!

[@lovekildetoft5658]

All other videos on gradient descent are atleast 20 minutes long. This one is five, and made me understand more than any of those videos. Thank you!

[@DataScienceGarage]

Thank you for watching! Hoping it was useful.

[@anamikabhowmick6322]

This is one of the best and easiest way to learn and understand gradient descent, thank you so much for this

[@DataScienceGarage]

Glad you liked it! :)

[@DataScienceGarage]

If you found useful in this video I highly recommend to check other related ones: -- Calculate Convolutional Layer Volume in ConvNet (kzbin.info/www/bejne/aaaolWN7p9Z6sLc) -- Adam. Rmsprop. Momentum. Optimization Algorithm. - Principles in Deep Learning (kzbin.info/www/bejne/j5LGgXh5pK5oibs) -- Numpy Argsort - np.argsort() - function. Simple Example (kzbin.info/www/bejne/bIibhnuso52Wock) -- Python Regular Expression (RegEx). Extract Dates from Strings in Pandas DataFrame (kzbin.info/www/bejne/e2XEp3WOl7OCfcU)

[@impzhu3088]

That’s the way to explain a concept! Example with detailed steps. Thank you so much!

[@DataScienceGarage]

Thanks for watching! :)

[@phaniraju0456]

I bow to your for this great clarification ..loved it

[@riki2404]

Thank you for such a clear explanation. Short and precise. no unnecessary talk.

[@DataScienceGarage]

Thanks for watching! Hoping it was useful :)

[@sukanya4498]

Love this video ❤️, Very simple and precise! Thank you !

[@DataScienceGarage]

Thanks for watching! :)

[@TingBie]

Thanks for this example, simple and spot-on!

[@eramitvajpeyee85]

Thank you so much for explaining it in short and easy way!! Please keep uploading content like this.

[@DataScienceGarage]

Thank you for watching! Glad you enjoyed! :)

[@dennisjoseph4528]

Great job of explaining this as simple as possible Sir

[@samvanoye]

Perfectly explained, thanks!

[@DataScienceGarage]

Thanks for watching! :)

[@abdelrahmane657]

Oh my god, you are excellent. You make the difference on KZbin. Thank you so much. 🎉🙏👏🙌👌👍✌🏼

[@DataScienceGarage]

Thanks you for such feedback, appreciate it! :)

[@hindbelkharchiche1654]

Thank you .. the explanation is as simple as useful .

[@nawaab9275]

thanks for saving the semester

[@Elementiah]

Thank you so much for this! This is the perfect explanation! 😄

[@ajaykushwaha4233]

Best explanation ever.

[@wtry0067]

It's very short and very useful.. I get clarity what I was looking for. Thanks once again.

[@abdanettaye8217]

good starting, thank you

[@aalishanhunzai]

Bro thank you so much for your efforts, couldn't find a more simple explanation of gradient descent than this one.

[@DataScienceGarage]

Thanks for such feedback! :)

[@muhammadhashir7949]

Thank you so much your work was practical and I loved it alot and underestood gradient descent. Before that I spent lots of time but didn't understood it properly

[@omkarkadam5715]

Thanks mate, Finally Enlightened.

[@DataScienceGarage]

Thanks for watching! I hope it was useful :)

[@supantha118]

Thank you so much

[@mastan775]

Very good explanation...thanks a lot.

[@abdellatifmarghan7521]

Thank you. grateful explanation

[@DataScienceGarage]

Glad it was useful! :)

[@zafarnasim9267]

You made it so simple. Great Job!

[@DataScienceGarage]

Thanks a lot! :)

[@moazelsawaf2000]

Thanks a lot sir

[@josephsmy1994]

Awesome explanation! straight to the point

[@DataScienceGarage]

Thanks for such feedback! :)

[@basheeralwaely9658]

Well done sir, very easy to understand

[@RayhanAhmedsimanto]

Amazing Practical Explanation. Great work.

[@DataScienceGarage]

Thanks Rayhan!

[@yasamannazemi6706]

It was so simple and helped me a lot :) Thanks👍🏻

[@DataScienceGarage]

Thanks!

[@murat2073]

thank you Sir! you are a HERO!!!

[@DataScienceGarage]

Thanks a lot! :)

Perfect !

[@davidkayode6679]

Wonderful Video!!! Thank You!

[@DataScienceGarage]

Thanks for feedback!:)

[@TheJayenz]

Thank you so much!

[@DataScienceGarage]

Thanks for watching! :)

[@Alex-pd5xc]

wow dude, very clearly explained and you made it simple for me to understand. cheers man

[@DataScienceGarage]

Thanks for such feedback, appreciate!

[@jimyang8824]

Good explanation!

[@darkman8939]

thanks, very hhelpful.

[@luisurena1770]

Coñazo siempre hay un indu que me ayuda a entender todo🔥🔥🔥🔥

[@muhammadhilmirozan1266]

thx for explanation!

[@DataScienceGarage]

Thanks for watching! :)

[@9891676610]

Awesome explanation . Thanks a lot !!

[@DataScienceGarage]

Thanks for watching! Hope it was useful!

[@george4746]

Thanks, It was very clear and concise.

[@DataScienceGarage]

Thanks!

[@Slendich]

Really great and simple explanation. Thank you

[@DataScienceGarage]

Thanks for sich feedback, I'm glad it was helpful! :)

[@strykeregziadahmed9562]

2 hours in DL course i didnt get it 5 min made my day this is how actually learning should be

[@DataScienceGarage]

Glad it was helpful for you! :)

[@Snetter]

Nice work! thanks

[@DataScienceGarage]

Thanks for feedback, glad for this! :)

[@johnnyslater3455]

Thank you!

[@DataScienceGarage]

Thank you for watching!

[@mohamedelkhanche707]

ohhhhh wonderful i was chocked this is insane thank you from all my heart

[@DataScienceGarage]

Thanks a lot for such feedback, appreciate!

[@bhavikdudhrejiya4478]

Very good video. I appreciate your hard work. Keep uploading more videos.

[@DataScienceGarage]

Many thanks for such comment!

[@diegososa5280]

Thank you very much!

[@DataScienceGarage]

Thanks! Hoping it was useful. :)

[@mbogitechconpts]

Beautiful video. I have to like it.

[@DataScienceGarage]

Thanks for feedback, inspiring!

[@pwan3971]

Thanks a lot, really appeciate the video, this makes so much sense now

[@DataScienceGarage]

Thank you for you feedback! Glad it was useful for you :)

[@AlfredEssa]

Good job!

[@praneethcj6544]

Simple and clear ... Yet need more detailing ...!!!!

[@Hasasinful]

Thanks just what i needed

[@DataScienceGarage]

Hope it was useful. Thank you!

[@AJ-et3vf]

Very useful! Awesome ❤️

[@DataScienceGarage]

Thanks for watching! :)

[@karthiklogan9384]

really helpful sir.thank you so much

[@DataScienceGarage]

Happy that was useful.

[@machinelearningid3931]

Thanks, this give me the light in darkness.

[@radhar5349]

Great explanation. Easy to get the concept

[@DataScienceGarage]

Thanks for feedback! :)

[@eliashossain4327]

Best explanation.

[@DataScienceGarage]

Thanka for such feedback! :)

[@bhavya2301]

Thankyou.

[@fmikael1]

Thanks for the great explination. everyone always complicates it

[@DataScienceGarage]

Thanks for feedback, glad it was helpful! :)

[@thankyouthankyou1172]

Useful thank you

[@DataScienceGarage]

Thank you for watching!

[@MrAnindyabanerjee]

Thank you

[@yaminikommi5406]

We can take any number as intial parameters and learning rate

[@sanurcucuyeva7040]

Hi, thanks for explanation. If our function is hard, at what point in the iteration should we stop to find the minimum point

[@MuditDahiya]

Very nice explanation!!

[@DataScienceGarage]

Thanks!

[@hemantsah8567]

It is easy... spent my 2 days on learning gradient descent.... then I came to your video... Thanks bro

[@emrecik9882]

Thanks

[@arvinds7182]

On point👏

[@DataScienceGarage]

Thanks!

[@seathru1232]

GREAT!

[@DataScienceGarage]

Thanks!

[@govardhan3099]

Great explained...

[@DataScienceGarage]

Thanks!

[@mohsinjunaid8454]

thanks

[@user-qj1lm1xh2z]

Well done 👏

[@DataScienceGarage]

Thanks! :)

[@SuperYtc1]

This is a good video.

[@DataScienceGarage]

Thanks!

[@grinfacelaxu]

Nice!

[@DataScienceGarage]

Thanks!

[@alejandrobarquin6511]

I get the idea but any reason for not doing simple math to find the local min? dy/dx = 2(x+5) If we want to find the min, we just do dy/dx=0.. then: 0 = 2(x+5) x = -5

[@bernardaslabutis5098]

Ačiū, padėjo!

[@DataScienceGarage]

Džiaugiuosi! :)

[@glenfernandes253]

how do you know, how many iterations to run before reaching the global/local minimum, what if it reaches the minimum and starts climbing on the other side ?

[@ydkmusic]

Great video! There is a typo around 3:50. The bottom equation should be x_2 = .... instead of x_1.

[@smurfNA]

hey! so do we choose the learning rate? and the gradient is simply just the function right ?

[@dtakamalakirthidissanayake9770]

Thank You So Much. Great Simple Explanation!!!

[@sandipmaity2687]

Amazing Explanation :) Really simple and to the point 😀

[@blinky1892]

How do we know what the y value is of the parabole at any given x?😊

[@mattk6182]

using x as your means of showing multiplication is confusing, makes it looks like you took the derivative wrong with 2x(x+5)..maybe in future videos either leave the x out so the multiplication is implied.

[@twicestay6683]

Thx a lot!!! But I'd like to ask why the learning rate=0.01? is it a random number? Thx

[@louerleseigneur4532]

merci

[@kronlogic2408]

For the Iteration 2, shouldn't the second line be x2= and not x1= ?

[@bharatcreations7154]

Can we compute same thing without getting into learning rate??

[@denisplotnikov6875]

How to use this example for Stochastic Gradient Descent?

[@michaelscott8572]

What I don't get is: When we use this method in neural net, we don't know the Errorfunction. We just have some point. So how can I build the derivative?

[@preetbenipal1034]

its simple short and easy here ..thank you :)

[@gireejatmajhiremath6751]

Thank you very much sir for clearing my concept.

[@colton3000]

How do we find learning rate?

[@senthilkumar1172]

super

[@DataScienceGarage]

Thanks for watching!

[@pearlsofwisdom2416]

Good explanation but would have been better if you elaborated its formula of why it is used to reach next step. Why is derivative multiplied by learning rate and why it is then substracted from first point value

[@blendaguedes]

The learning rate makes the "decay slow". At his first interaction, the result would be: -3 -4 = -7. Can you see where this is going? As he goes slow he will keep dropping his "y", until he get to as close as possible to -5. Sometimes to get at the minimum you have to make you learning rate smaller while computing your weights .

[@davidbarnwell_virtual_clas6729]

How do we choose the learning rate? Good video but it's things like that I'd love to know

[@DataScienceGarage]

Hi! Choosing learning rate often is not easy task. I usually makes experiments on model performance with multiple learning rate (manual, Grid search hyperparameter tuning, Bayesian search, etc.).

[@davidbarnwell_virtual_clas6729]

@@DataScienceGarage Ahh...ok...I get you...it's very interesting.

[@codingtamilan]

How you draw that curve can be fixed as -5 ? Always it is centre as -5 ?

[@blendaguedes]

First you decides witch will be his loss function. On his case it was (5+x)^2, or x^2 + 10x + 25. Nos you program the gradient descent to find the minimum of the function. It depends of your function.

[@codingtamilan]

@@blendaguedes thq... pleasure to meet you

[@shankaks7217]

Why did we choose 0.01 as the learning rate?

[@boniface385]

Hi, why the learning rate are 0.01? Can it be any random learning rate? For example 0.2, 0.02 or any. I appreciate it for thee fast reply, thank you😊🙏🏻🙏🏻🙏🏻

[@DataScienceGarage]

Hello! Thanks for watching this video, I'm glad it was useful for you. While modelling ML system, you can specify random Learning rate. However the good practice is to use 0.1, 0.01, 0.001, or 0.0001. Each ML model has its own architecture, and different training data, hyperparameters, etc., so learning rate can be adopted separately for each case. Here, I used 0.01 just for demonstration purposes.

[@boniface385]

@@DataScienceGarage thank you so much for the explanation. 🫶🏻