DStream vs RDD

Deep Neural Net simple explanation (NN 1)

Nueral net understanding.Draw a line that seperates blue and red shackles

matrix transpose example (DL)

Transpose

Getting the transpose of a matrix is really easy in NumPy. Simply access its T attribute. There is also a transpose() function which returns the same thing, but you’ll rarely see that used anywhere because typing T is so much easier. :)

For example:

m = np.array([[1,2,3,4], [5,6,7,8], [9,10,11,12]])
m
# displays the following result:
# array([[ 1,  2,  3,  4],
#        [ 5,  6,  7,  8],
#        [ 9, 10, 11, 12]])

m.T
# displays the following result:
# array([[ 1,  5,  9],
#        [ 2,  6, 10],
#        [ 3,  7, 11],
#        [ 4,  8, 12]])

NumPy does this without actually moving any data in memory - it simply changes the way it indexes the original matrix - so it’s quite efficient.

However, that also means you need to be careful with how you modify objects, because they are sharing the same data. For example, with the same matrix m from above, let's make a new variable m_t that stores m's transpose. Then look what happens if we modify a value in m_t:

m_t = m.T
m_t[3][1] = 200
m_t
# displays the following result:
# array([[ 1,   5, 9],
#        [ 2,   6, 10],
#        [ 3,   7, 11],
#        [ 4, 200, 12]])

m
# displays the following result:
# array([[ 1,  2,  3,   4],
#        [ 5,  6,  7, 200],
#        [ 9, 10, 11,  12]])

Notice how it modified both the transpose and the original matrix, too! That's because they are sharing the same copy of data. So remember to consider the transpose just as a different view of your matrix, rather than a different matrix entirely.

Companies know more about you

People concerned about privacy often try to be “careful” online. They stay off social media, or if they’re on it, they post cautiously.  By doing so, they think they are protecting their privacy.

But they are wrong. Because of technological advances and the sheer amount of data now available about billions of other people, discretion no longer suffices to protect your privacy. Computer algorithms and network analyses can now infer, with a sufficiently high degree of accuracy, a wide range of things about you that you may have never disclosed, including your moods, your political beliefs, your sexual orientation and your health.

There is no longer such a thing as individually “opting out” of our privacy-compromised world.

What is to be done? Designing phones and other devices to be more privacy-protected would be start, and government regulation of the collection and flow of data would help slow things down. But this is not the complete solution. We also need to start passing laws that directly regulate the use of computational inference: What will we allow to be inferred, and under what conditions, and subject to what kinds of accountability, disclosure, controls and penalties for misuse?

Until we have good answers to these questions, you can expect others to continue to know more and more about you — no matter how discreet you may have been.

One Hot Encoding

One hot encoding is a process by which categorical variables are converted into a form that could be provided to ML algorithms to do a better job in prediction.

CATEGORICAL DATA

Lets take a dataset of food names. In this dataset, if there was another food name it would have categorical value as 4.As the no of unique value increases, the categorical values increases.

Slideshow using Notebook

The slides are created in notebooks like normal, but you'll need to designate which cells are slides and the type of slide the cell will be. In the menu bar, click View > Cell Toolbar > Slideshow to bring up the slide cell menu on each cell.

Data dimensions

SCALARS

• They have 0 dimensions
• Ex a persons height would be a scalar

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