Application of AlexNet|AlexNet layers| Matlab implementation| deep learn...

This video is all about how to make use of the alexnet for detecting the 1000 different object. Have discussed the success and failure of the applied method with examples.



The dataset is 50000 different images. For training images have reserved 60% of the dataset and next 40% is divided into testing and validation equally. To understand what is testing and validation watch the video in link https://youtu.be/upstQZm8LR4



The Alexnet is fully configurable network based on the applications. The instance of it shown by taking the help of a tool called as MATLAB.

The deeplearning, machine learning can be the reason for 4th industrial revolution in the future days such as driverless cars reduces the accidents, traffic jams, can replace humans for precise operations and still more.





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#nonlinearandlineardataset #regression #non-linearregression



Last video is on how to configure the AlexNet, to understand the different layers, Please watch: https://youtu.be/fHZCOJbzp_k

How to configure AlexNet|Deep learning|Neural network| Difference betwee...

Tried a new making of self-explaining video which explains about the configure of AlexNet for understanding of deep learning.

It has confined the difference between the machine learning and deep learning.

Watch the full video on below link

https://youtu.be/fHZCOJbzp_k



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What is linear and nonlinear dataset|how to classify them| machine learning

This video is about understanding what is linear and non-linear data.

This briefs about how to find the solution when dataset is linear or non-linear.

The steps for linear data

1. Find upper boundary.

2. Find lower boundary

3. Mean the both boundaries to obtain optimum boundary line.

For non-linear dataset

1. Increase the dimension through shifting

2. Shift the datapoints to one plane (towards right or left plane)

3. Square the shifted signal hence non-linear dataset converts to linear dataset.

While increasing the dimension take care of this, there is a rule that if you have N classes then increase the dimensions to N-1.

Find the boundary line which is not too complex. Have simpler lines for good accuracy.

#nonlinearandlineardataset #regression

How to use confidential mode in gmail mobile|resend the mails in gmail

How to use confidential mode in Gmail mobile application.

To resend the mail the gmail if the typo mistake has happened. The undo time can be set in between 5 and 30 seconds. This is the buffer-able time given for the user to correct the mail and resend the mail. The access to viewing the attachment can be decided by the sender by setting the expire date it can be a week, a day or one month. After the specified time the content inside the attachment can't be viewed.



A good feature added to the gmail.



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Last video is on what is over and underfitting in machine learning and its solution .

Please watch: https://youtu.be/6pF7uBVvd1c

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Overfitting and Underfitting With Machine Learning Algorithms|avoid over...

The video is all about what is underfit and overfitting in
machine learning.

The tips are provided for how to avoid overfitting.

The accuracy need not to be 100% but the practically lesser
loss algorithm becomes famous one.

Underfit occurs when we choose a simple straight line as the
differential boundary between two classes.

Overfit occurs if the differential line accurately matches
the features of data set.

To overcome overfit we can just decrease the complexity of
boundary line.

Increasing the number of dataset doesn't help in both the
cases of underfit and overfit.

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Last video is on compare between common algorithm and
machine learning algorithm.

Please watch: https://youtu.be/YyHRhUo2lHQ

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What is the difference between normal algorithm and machine learning alg...

How the machine learning algorithm responds to the problem and how they learn and difference between them.

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Last video is on what is training, validate and testing in machine learning and what is unseen data.

Please watch: https://youtu.be/upstQZm8LR4

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What is training validation test ratio in machine learning