Online or Offline Lectures!

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ONLINE OR OFFLINE?

After Coursera, Udemy and various online teaching platforms, our prestigious colleges started to think of taking online classes which ended a long summer- rainy break for every student who was eating, sleeping, Netflix the whole day.

Now, this schedule has been shifted to lecture-eat-sleep-Lecture. 

After the arrival of these online platforms to study, the demand for online teaching increased after youtube, Coursera, Udemy and various other platforms started to reach to students for different courses or small, simple topics. 

Why students preferred these online lectures? They could do it anywhere, anytime, pause, see it at 2x and many other options were opened up. The students wanted to learn at their convenience so they chose this. 

While being in a school or in a college, 

The big question here is, Are online Lectures the same as offline teaching?

Taking up the similarities we come up to the point that we sleep (toppers aside) and yeah! we are fed up of assignments in both of them. Just sitting and listening without any response has been one of the best parts of any class. Be it offline or online we always sleep late and wake up late w.r.t faculty of the 1st class. 

 

These all points were for against the classes but in favour of the classes, I would like to say that "WE DO STUDY" let it be only for our tests and exams and lately, the number of these assignments and tests have increased soo much that you can assume our amount of study.

Talking about the mischiefs we do in both the type of classes were different.

In the normal classes, we used to shift to the last benches and start humming, making various noises or just quietly ignoring the teachers.

In these online classes, it has been on different levels. Starting from keeping your mics on, and inviting our friends in the classes to do some unique stuff with the video cam on. Everyone would have seen many instances of these kinds of stuff in memes.

Students in the first class

EXAMS!

Yes! They are going too well with small preparations and lots of cheating via our famous WhatsApp university which has helped a lot no. of students to excel in exams and so we are thankful to our Online classes and its methods.

If we get a little serious here for attending these classes with great concentration, it has been tough for us. There have been instances where teachers take classes more than 1.5 hrs also(guidelines have been given by the government to not take a class for more than 45 mins). 

Various problems have also come up with students related to poor connectivity or unavailability of gadgets(There have been funds collected for these purposes and the facility has been provided to students which is rather a very good initiative taken by our college). Apart from this, teachers have been also cooperating with students by giving buffer days for tests and giving more days for assignments. Some of our teachers record their lectures so that students can watch them if there is less connectivity. So, we are really thankful to these teachers for their efforts for cooperating with students.

Yes, both Teachers and the Students have been frustrated by these online ways in some or the other way and from our students. But, on behalf of the students, we want our colleges to be opened and live our normal life rather than being in front of laptops the whole day.

The offline classes are far way better than these online classes and I am too waiting for colleges to reopen and be in the hostel and enjoy with friends and learning from the offline classes is something different and I hope everyone agrees to it!

Hoping to return to our college soon.

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What is bias ,variance, Overfitting and Underfitting in machine learning

What is bias ,variance, Overfitting and Underfitting in machine learning??

In this blog we are going to discuss the important topic of bias and variance and also about the Overfitting and Underfitting.

Lots of people train different machine learning models but they find difficulty in increasing the model accuracy due to the lack of knowledge of bias and variance.

In this guide we will walk through this terms and what does it means and by the end of the blog you would be able to increase your model accuracy

You can simply assume bias is a error of training data similarly variance is the error of testing data.

Things might not be clear till now right??

Assume there is the data set of  10000 images for training the model and 2000 images for testing the model.

If the model performs well at the training images and  not at the testing images this causes the problem of Overfitting. This means that accuracy in terms of training images is far greater than the testing images. So the performance of the model is good on training images but not for the testing images (data which have not seen by our model).

If the model does not perform well at both training images and testing images this causes the problem of Underfitting. This means that accuracy for training and testing images is not good  enough which affects the performance of the model.

So now how can we related BIAS and VARIANCE ?

Previously we discussed what is bias and variance

BIAS : error of training data

Variance : error of testing data

So in case of overfitting, model performs good at training images that means the error for the training images is less so it causes LOW BIAS and for the testing images the performance of the model is not good enough so it leads to HIGH VARIANCE.

Similarly in case of underfitting, training and testing accuracy both are less so there is high error for the training and testing images which leads to HIGH BIAS and HIGH VARIANCE.

Therefore error for training and testing images should be low which means our model must have LOW VARIANCE and LOW BIAS for the better performance of the model.

There are various optimization, regularization and initialization techniques which makes our model to move towards LOW VARIANCE and LOW BIAS and that makes our model to perform well with greater accuracy.

This all techniques will be discussed later.

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