Regarding setting initial parameters and learning rate in Regression

Hello Nilesh,

The gradient for logistic regression with binary cross entropy loss function is as given below:



f_{\vec {w},b}(\vec {x}^{(i)}) = sigmoid(w.x +b)


Ok sir now my concepts is crystal clear thank you very very much sir

You are most welcome @NILESH_RANJAN_PAL :blush:

Hello @shanup

I have successfully completed the 1st Course ,completely. What should I do next ? Should I start the next course or can you tell what should I do ?

*****Also can I show you the Logistic regression code ?

Congratulations Nilesh, on completing the first course!!

Ideally, you should move on to the next 2 courses and try and complete it. The first course has set you up nicely to enter into the world of deep learning, which is the happening place in Machine Learning.

The 3 courses in the Machine Learning specialization will give you a well-rounded intro into many of the important topics. Once you finish this specialization, then you would be in a better position to take a call on whether you like this field, if you want to go deeper into it and take up some of the cutting edge stuff.

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Hi @shanup
Ok thank you sir , I have already decided to stay in this field from start .

Also I want to show you the rectified code of Logistic Regression once to you ? Please can I show ?

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Sure. Please send me a DM and i will take a look.

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Keep it up and Happy New Year! :tada:

Thanks @Christian_Simonis

Happy New Year , to you too.

Probably a nice way to end the year :grin: