Loss Functions in Deep Learning | Deep Learning | CampusX

In this video, we'll understand the concept of Loss Functions and their role in training neural networks. Join me for a straightforward explanation to grasp how these functions impact model performance.
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✨ Hashtags✨
#DeepLearning #LossFunctions #NeuralNetworks #MachineLearning #AI #LearningBasics #SimplifiedLearning #modeltraining
⌚Time Stamps⌚
00:00 - Intro
01:09 - What is loss function?
11:08 - Loss functions in deep learning
14:20 - Loss function vs cost function
24:35 - Advantages/Disadvantages
59:13 - Outro

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[@anuradhabalasubramanian9845]

Fantastic Explanation Sir ! Absolutely brilliant ! Way to go Sir ! Thank you so much for the crystal clear explanation

[@kumarabhishek1064]

When will you cover RNN, encoder-decoder & transformers? Also, if you could make mini projects on these topics, it would be great. Keep doing this great work of knowledge sharing, hope your tribe grows more. 👍

[@pratikghute2343]

the only channel I have ever seen on youtube is underrated! best content seen so far....... Thanks a lot

[@anitaga469]

Good Content, great explanation and an exceptionally gifted teacher. Learning is truly made enjoyable by your videos. Thank you for your hard work and clear teaching Nitish Sir.

[@abhaykumaramanofficial]

Great content for me....now everything about loss function is clear .......thank you

[@HirokiKudoGT]

This is the best explaination about the whole basic of losses , all doubt are cleared thank you so much for this video.

[@HARSHKUMAR-cm7ek]

Your Content deleviery is truely outstanding sir . Although the numbers don,t justify with your teaching talent but let me tell i came here after seeing many of the paid courses and became a fond of ur teaching method .So, please don,t stop making such fabulous videos . I am pretty sure that this channel will be among top channels for ML and data science soon !!

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[@palmangal]

It was a great Explanation . Thank you so much for such amazing videos.

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was able to understand each and every word, concept just because of you sir. Your teaching has brought me to this place where i can understand such concepts easily. Thank you very much sir. Really appreciate your hard work and passion. ❣🌼🌟

[@talibdaryabi9434]

I wanted this video and got it. Thank you.

[@paruParu-rc1bu]

With all respect....thank you very much ❤

[@farhadkhan3893]

thank you for your hard work

[@tanmayshinde7853]

These loss functions are the same as taught in machine learning. Difference in Huber, Binary and Categorical loss function.

[@amLife07]

Thank you so much sir for another amazing lecture ❤😊

[@KeyurPanchal0475]

Simple and easy to understand.

[@RanjitSingh-rq1qx]

Great work sir. Amazing 😍

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Amazing sir 🙏🏻

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hmesha ki trha kmaaaal Sir g

[@IRFANSAMS]

Awesome sir!

[@jayantsharma2267]

great content

[@Sara-fp1zw]

Thank you!!!

[@SumanPokhrel0]

Beautiful explanation

[@parikshitshahane6799]

Very very excellent teaching skills you have Sir! Its like college senior explaining concept to me sitting in hostel room.

[@sambitmohanty1758]

Great video sir as expected

[@rashidsiddiqui4502]

thank you so much sir, clear explaination

[@uzairrehman5765]

Great content!

[@Shisuiii69]

Thanks for the timestamps It's really helpful

[@rb4754]

Mindboggling !!!!!!!!!!!!!!!!!!

[@shantanuekhande4788]

great explanation. can you tell me why we need bias in NN . how it is useful

[@narendraparmar1631]

Very well explained, Thanks

[@uddhavsangle2219]

nice explanation sir thank you so much

[@mrityunjayupadhyay7332]

Amazing

[@faheemfbr9156]

Very well explained

[@narendersingh6492]

This is so very important

[@safiullah353]

How beautiful this is 🥰

[@pavangoyal6840]

Thank you

[@ParthivShah]

Thank You Sir.

[@rohansingh6329]

awesome man just amazing ... ! ! !

[@bhojpuridance3715]

Thanxs sir

[@Tusharchitrakar]

Great lecture as usual. Just one small clarification: binary cross entropy has a convex (but not close formed) solution hence it only has a single global minima and no local minima. This can be proved using simple calculus by noticing the second derivatives and check if it is always greater than 0. Hence, you mentioned that there are multiple local minima which is not right. But thanks for your comprehensive material which is helping us learn such complex topics with ease!

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Great work

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Learning DL and Hindi together, respect from Afghanistan Sir!

[@sanchitdeepsingh9663]

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[@kindaeasy9797]

amazing lectureeeeeeee

[@zkhan2023]

Thanks Sir

[@hey.Sourin]

Thank you sir 😁😊

[@RajaKumar-yx1uj]

Welcome Back Sir 🤟

[@ANKUSHKUMAR-jr1pf]

at timestamp 44:40 --> sir, you told that binary corss entropy may have multiple minimal, but binary cross entropy is a convex function so it won't have multiple minima, i think.

[@stoic_sapien1]

44:52 Binary cross entropy loss is convex function,it will only have one local minima or only one global minima

[@OguriRavindra]

Hi. i think the in huber loss example plot @ 36:59, it is for clasification example rather than regression example. regression line should pass through the data points instead of seperating them.

[@sumitprasad035]

🦸‍♂Thank you Bhaiya ...

[@techsavy5669]

Great concise video. Loved it. A small question 💡: Sometimes we do drop='first', to remove that redundant first column during onehotencoding. So does that make a difference while using either of these categorical losses!?

[@pratikghute2343]

I think this might be happening automatically or not needed bcoz that way we could not get the loss for that category

[@AmitUtkarsh99]

yes it affects the model because u should keep no. of parameters as less as possible for optimised model. but we dont always . it depends on variables or input. like 2 inputs can be interpreted by just one variable. 2^1. 3 variables require at least 2 variables but 2^2 is 4 so we can drop one column.

[@aakiffpanjwani1089]

can we use step function as the activation function for the last layer/ prediction node while doing classification problem using binary cross entropy? for 0 and 1 outputs?

[@ManasNandMohan]

Awesome

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sir carryon this series

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Galaxy tab s7+

[@vinayakchhabra7208]

best

[@74kumarrohit]

Can you please create a videos for remainig Loss Function , for AutoEncoders, GANS, Transformers also. Thanks

[@lakshya8532]

One disadvantage of MSE that, i can figure out if there are multiple local minima then there might be a case where MSE loss function can lead to a local minima instead of global minima

[@lonehawk4096]

ML MICE SKLEARN video is still pending sir pleases make that video, other Playlist are also very helpfull thanks for all content.

[@nxlamik1245]

I am enjoying your video like a web series sir

[@suriyab8143]

Sir, which tool are you using for explanation in this video

[@kindaeasy9797]

easyy thankssss

[@tejassrivastava6971]

Wouldn't Categorical and Sparse Entropy become same ? As after OHE, all log terms become zero except the current one which gives same result as from Sparse.

[@user-fr9fg3rf2h]

At 21:06,[MEAN SQUARE ERROR] In order to calculate totel error by doing [y - y^] some value may be negative and can reduce the error {That we don't want} that is why we are doing square after doing substraction as you said. So here my doubt is that can we make that negative value to positive. then there is no need to do square. Please explain this. Thank you. :)

[@vinayakchhabra7208]

maza aagya

[@ashwinjain5566]

at 36:27, shouldnt the line be nearly perpendicular to what you drew? seems like a case of simpson's paradox.

[@alokmishra5367]

❤

[@ariondas7415]

if the difference in (yi - y^i) is in decimals, then the loss value is diminished and not magnified, so maybe a novelty would be take this into account.

[@alastormoody1282]

Respect

[@shashankshekharsingh9336]

thank your sir for this great content. 13/05/24

[@KiyotakaAyanokoji1]

what is the difference : 1.) if we update the weights and bias on each row ,for all epoches , 2) for each batch (all rows togeather), for all epoches . can you tell senarios where one is better over other?

[@shashankshekharsingh9336]

+1

[@KaranGupta-kv6wq]

can someone explain me how 0.3 0.6 0.1 is coming @ 52:37 I want to know how can I get these values and which formula is used

[@sandipansarkar9211]

finished watching

[@anishkhatiwada2502]

please put timestamp for each topic in this video.

[@user-xw1eu7jx7n]

grate

[@kindaeasy9797]

22:25 unit^2

[@spyzvarun5478]

Isn't logloss convex?

[@sam-mv6vj]

Thank you sir for resuming

[@praveendeena1493]

Hi sir I want complete end to end project video.please share me

[@vishalpatil228]

43.32 cost function = 1/n∑ ( loss function )

[@ahmadtalhaansari4456]

Revising my concepts. August 04, 2023 😅

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Why you stopped posting videos in this Playlist?

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Creating the next one right now... Backpropogation

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Black bord achha tha

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aise explain karoge to like to karna padega na....

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Please take care of background noises

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Time series in details 😓

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Let him finish this series na. Why forcing like this???

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Birds ka voice aara background me

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First viewer

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Avoid Hindi speaking in video