Binary Cross Entropy Explained | What is Binary Cross Entropy | Log loss function explained

Binary Cross Entropy Explained | What is Binary Cross Entropy | Log loss function explained
#BinaryCrossEntropy #LogLoss #UnfoldDataScience
Hello ,
My name is Aman and I am a Data Scientist.
About this video:
This video explains the concept of Binary cross entropy. I have explained Binary cross entropy loss and log loss explained. I have explained the concept with stepwise mathematical calculation. Below topics are explained in this video.
1. Binary Cross Entropy explained
2. What is Binary Cross Entropy
3. Log loss function explained
4. Understanding Binary cross entropy loss function
5. Loss function Binary cross entropy
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[@satyashreerath4945]

Hi Aman, in corrected probability column you have not written correctly. You explained it correctly that corrected probability is that which takes the one class, then if u take let's say class 1 then for class zero corrected probability will be 0.7 and 0.8 in your example. Thank you.

[@UnfoldDataScience]

Thanks Satya, I will check out.

[@harshdwivedi7102]

Best video on Binary Cross-entropy , I have watched so many videos but nobody explained this specific point that we use probability for either of class and not corrected probability.

[@UnfoldDataScience]

Thanks Harsha for watching.

[@ajiteshthawait4771]

Loss function helps to optimize the problems. Loss function in classification. Calculate Actual probablity and corrected probability. Entropy= randomness in your data. Rows will helps to calculate corrected probability. Find the log values and the summation of all log values is the binary cross entropy. Probity of falling in either of the class. Of probablity of classification is higher, then lesser randomness.

[@letslearndatasciencetogeth479]

Thank you so much sir I was so eager for your explanation on this topics

[@AnishManGurung]

Another amazing explanation.

[@alwaysaditi2001]

thank you so much for the great explanation!!

[@UnfoldDataScience]

Thanks Aditi

[@jk-sm6qr]

Nice explaination

[@shubha07m]

Good explanation!

[@jasonbourn29]

Couldn't understand but appreciate the effort

[@technicalraman8848]

Sir Thanks for this nice explanation. Sir can u plz make a video on purpose of hidden layer and what is the use of hidden layer by real life examples.

[@sandipansarkar9211]

finished watching

[@UnfoldDataScience]

Thanks Sandipan again.

[@navoditmehta8833]

Well explained....using the graph of this function could be more helpful.

[@sanjeevkumarb8416]

what is the base of the log to be used ???

[@rajkir2852]

Thanks sir, you are doing amazing job. Whiteboard aur thora bada kijiye sir maza ayega

[@skvali3810]

Hi aaman how to Optimize the loss for classification problem. like in regression we use gradient descent for M and C value, like that is there have any Optimizers for classification .... This is a interview question can you please help me on that

[@jackiemease4018]

Great video! I am not 100% sure I understand when you have the log of the probabilities in the right side of your table, which probability that is the log of. Is it original or corrected or some combination of both. Thanks in advance!

[@UnfoldDataScience]

I will have to check,can't answer immediately

[@letslearndatasciencetogeth479]

Hello Sir! Please explain why cannot loss function used in linear regression be used here in this case in details?? Also, how loss function is used to reach the global minima. Thanks a lot for the wonderful explaination

[@rajkir2852]

The job of loss function is used to calculate how much you are far away from actual value i.e predicted minus actual value will give you that. Then use gradient descent, its a optimization algo and it takes value of loss function and some parameters and gradient descent helps to reach global min

[@letslearndatasciencetogeth479]

@@rajkir2852 thanks for the explanation

[@rajkir2852]

Sir, also waiting for categorical cross entropy plz

[@dr.bheemsainik4316]

sir, what is the range of cross-entropy? and what is the unit of cross entropy?

[@AftabGaming-st3tq]

Bhai hindi me bolta to tera kia jata

[@UnfoldDataScience]

:D