Tutorial 4: How to train Neural Network with BackPropogation

In this video we will understand how we can train the Neural Network with Backpropagation.
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[@2die4mario]

This is really simplified. Greatly appreciated. Much better than those university professors who get obsessed in the math without showing the audience the big picture !!

[@anketsonawane6651]

Yup! They uses lot of symbols... Sometimes even its hard to remember those symbols

can't agree more!

[@Ranjithbhat444]

Beautiful, being a professor myself I feel often people fail to focus on the big picture ; it is like we are thinking the local minima as a global minima 😅

[@priyanath2754]

I have taken courses in other platforms, but I must say, the simplicity I found in your explanation helps me grab the topic much easily🙏🙏🙏

[@aneeshkrishna4375]

I have paid fortunes to do masters in data science and ended up watching your videos. You deserve a nobel prize for all these videos.

[@devgak7367]

you just nailed it. Simplicity of his explination is unmatched anywhere. Thanks you sir.

[@ShravaniDeshpande-xw5bu]

finally found a video, that helped me understand this topic. its relief + satisfaction

[@varunss9057]

I am new to deep learning. The content you provide helps me understand the concept from the very basic and this clarity i could not find in any videos. Keep up the good work!!!!!You are doing a great job.

[@anikethdeshpande8336]

The best explanation on NN I've ever seen so far. Thanks for going ahead step by step and explaining in simple words

[@mohitrock100]

Great Video Sir. I haven't seen such a simple explanation of such a brainstorming concept. Many people just take days to understand Backpropagation and here you have cleared my concepts withing 10 minutes.

[@indirasakthivel716]

You are genius!!! No other instructor can teach in 10 minutes this complicated concept👏👏👏 Thank you🙏

[@sanchitsawant4450]

Sir I don't know why but whenever I watch your videos I get motivated to go in the more depth of that particular topic.Thank u sir sharing such valuable content for free .

[@arpittiwari6590]

Sir I had watch more than 10 videos but didn't understand now in 9 mint I had understood very well. Simply awesome!!!!!!!!!!!

[@isiomalisaclement6254]

This channel is a discovery...you have been able to cut through all the jargon and make these theories less abstract. Thank you very much

[@harshaddeshmukh2935]

Krish sir you are really amazing! You have made difficult concepts simpler! Our teachers are not able to do this, but you have done it very well! Thanks for being with us in this learning journey!

[@mhdomarbahra1678]

magnificent explanation the simplicity is perfect hard is made easy with you thanks!

[@sarkandawale]

Sir there is one thing for sure to tell u, your way of teaching is so relatable and hence easy to understand, Also very effective, I've went through so many tutorials and lectures but it is all making sense now in your video, I m very thankful to u sir keep teaching,. u r a great teacher.

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Finally the stuff I had been looking for. Simple and to the point.

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Awesome playlist. Thank you for sharing your knowledge 😊🤟

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Your are great. The way you explains, anyone can understand. Thank you.

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I came here after watching Coursera course and I think it's more clear and magnificent 🤤

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Exactly

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Really

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so far you are the best teacher.

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khatrnak sikhata he ye banda...... ek bar dekh lo pure dhyan se fir nind mai bhi koi puch le no issue ....chhap jata h dimag mai ......

[@adityashewale7983]

hats off to you sir,Your explanation is top level, THnak you so much for guiding us...

[@shikhajangra2760]

thank you so much sir, your way to simplify this problem is very nice.👍👍👍👍👍👍thanks again sir.

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Clearly the best DL and ML teacher!

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Underrated content! Keep up the good work! 💯

[@mbmathematicsacademic7038]

this guy explained optimzation,backpropogation and learning rates all in few mins

[@naresh8198]

you have explained it in the simplest way ,Thank you !

[@cybergame.]

Sir your videos are much more better than coursera courses...........Thank You.

[@_al00sk]

Your style of explanation is helping me a lot - thanks for these videos!

[@iamdare]

Wow man, you’re a blessing. Thank you for this great teaching, you simplified everything.

[@hari.prasad_]

This course is simple and clear than most of the courses out there.

[@kosk1997]

The best video so far. So clear and crisp. Hatsoff sir!!

[@commonboy1116]

Way you have explain complex topic in such simple way ......

[@suryakirannvs]

Thank you Sir. Amazing.!!! Derivative: Ratio of change of dependent variable w.r.t independent variable

[@Jane-ce2dq]

Krish thanks for work. Tutorial 4 supposed to be Activation function part 2.

[@RS-el7iu]

the best explanation...simple n straight to the point. thanks alot

[@indranisen5877]

Excellent, you are our confidence!

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bro all these videos simply tells how deeply you have the knowledge of deep learning

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perfectly explained !!! Simple and to the point !... Kudos ....

[@AgaGrusz]

You are what I needed. Thank You soooo much :)

[@fet1612]

@Krish Naik How methodical your work is! Brilliant! Keep it up. Your videos clarify things lucidly.

[@RanjithKumar-jo7xf]

Nice Explanation, I like the way you teach.

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Amazing work, sir gi, love it, And Thank you for such a concise and understandable explanation.

[@liass6354]

thank you for making this video! hope I can get a good grade in next week exam on AI

[@priyasanthakumara6520]

Amazing....great job with lot of thanks....

[@dushyantsingh4278]

Thank you sir for clearing all the doubts by this video

[@almassaba9377]

This was amazing. Thankyou Kris. Thankyou for existing.

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Kris 🤡

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best trainer i have ever seen....

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what an Explanation !!!! amazing

[@user-xd4cl3qd8x]

Well done bro, simple and precise

[@w.a.imadhusanka1578]

The things taught are well understood.thank you sar🥰🥰

[@yashdeshpande9202]

I think that , during the probleme discussed about pass or fail i.e. binary classification the loss function should not MSE . It should be cross entropy loss i.e. -ylog(y)+(1-y)log(1-y)

[@fthialbkosh1632]

Thank you, Sir, for your sharing, with perfect explanations.

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you make this topic seem so easy!!!!! Thank you!

[@mashroorsakib4006]

Awesome explanation sir. Thank you for sharing your knowledge

[@abdulqadar9580]

Thank you Sir for your great efforts

[@citoyennumero4434]

Simple et concis, je vous remercie pour l'explication :) :)

[@Qutybar]

You deserve 1M like

[@mohdazam1404]

Damn good explanation .... One question, how we choose learning rate??

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Mahn. You are too good

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the man, the myth, the legend

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Thanks for turning off the fan! :D

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Awesome explanation 👌

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maza aa gaya...awesome

[@anshi6205]

Your lectures is amazing and very helpful but you looks so serious in every video

[@bilalghauri6516]

(1):why we use loss function (2): how should we know that the loss value is minimal or increased (3): where should we find the learning rate?

[@sriramswar]

Hi Krish, What are the ideal Learning rates that need to be used? How do we decide which Learning rate value is ideal for a Neural network?

[@krishnaik06]

Yes it will be mentioned in the upcoming videos

[@vishaljhaveri6176]

Nice video. Thank you sir.

[@manukhurana8042]

hi krish , At 3:30 you said we can do square to make it +vs , we can also use |mod| to make it +ve.

[@satirthapaulshyam7769]

Btw how the loss function has defined.like lets suppose u have given a random weight first and depending on that u have get a average lost value for all the train sample.so for a specific set of weight u r getting a specific lost value than how u r getting a function.cz u have just got a specific output for a specific input.it Doesn't make anysense about the costfunction

[@sandipansarkar9211]

That was a superb video.But now things are getting tougher and tougher.Need to cope up with.

[@nuamanjaleel5430]

Sir could you please explain back propagation with an example, It would be of great help as that's the part where most of us make mistake

[@praneethaluru4801]

Literally great explanation brother.

[@girish2555]

Simply great 🙏

[@hashimhafeez21]

pretty great explanation

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simply the best

[@miteshmohite7829]

I want to know how does each observation gets trained over here you have used just one one observation

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thank you very much sir!! u gained a subscriber.

[@sajidalam1989]

well explained!!

[@satishkundanagar3237]

Could you please clarify my following doubts? Thanks in advance. 1. Are you using any affine function and/or activation function in the output layer node in order to calculate y_hat? Reason being, weight W4 is passed as input to the output layer node and no details are mentioned about the usual two step process that take place in every node of the neural network i.e. an affine function (where weights are actually used) and an activation function. 2. Is it or is it not the cost function is average of the loss function for individual training samples? Cost function is defined as summation of loss function in this video and not average? 3. I'm not clear on why propagation helps in tuning the model parameters. Back propagation and Gradient descent work together in tuning the weights. Mathematically and geometrically I'm not convinced with the statement "back propagation is used to train the weights of a neural network".

[@zindankurt1289]

Actually, I think all of y will pass through all neurons. Therefore, the final output will be calculated over the y_hat = w4*(y_prev). When we include the biases in this calculation, the output from a neuron in a layer will be y_prev = (W1*X + biases) -> y_nextneuron = (W2*y_prev + biases).

[@AmitYadav-ig8yt]

Thank you, Sir, May you please make videos on Unsupervised ML algorithms ?

[@manjotsingh7602]

but why you take regression error as loss function as example was of binary classification & likelihood error will be used

[@rohitjagdale4648]

Excellent explanation !!! One question : what happens to biases in backward propagation ?

[@jessiedavis2628]

Good explanation

[@RishikeshGangaDarshan]

In classification the loss function should be different like log loss here sir you use regression loss function please correct if I am wrong

[@RAZZKIRAN]

GD vs SGD vs PSO vs GA ? please give the efficiencies of these optimizers?

[@bibhupadhy4155]

Krish, I think it would have been better if you would have taken , Regression example and explained it , Coz the loss function you are showing over here is squared error, which is not the correct loss function for a classification problem, rather binary cross entropy is . May be you can rectify it. :). Even the cost function , You forgot to divide it by number of samples .

[@thundertech4025]

Thank you so much sir

[@rishujazz.7006]

Thank you sir

[@akiya2112]

thanks sir but I have one question how can I get w1,w2,w3.... from the beginning?

[@sriramanramalingam9892]

Sir, thanks for providing wonderful videos. Sir if possible could you provide a basic explanation for construction of neural networks in equation format (in first order differential equations) including state vector, activation functions. Thank you sir

[@akashgarg5770]

Great Video. Sound is too low, please increase sound

[@shahrzadamini140]

It was perfect. Thanks a lot

[@subhadipghosh8194]

@Krish Naik As this is a classification problem, how can it use squared error as a loss function?

[@madanmohanpachouly6135]

Good explanantion

[@Jane-ce2dq]

Very clear