Monday, October 28, 2019

Deep learning and CNN model used to output steering angle to autonomous vehicle

Git hub repo for this project :

Deep Neural Net simple explanation (NN 1)

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

matrix transpose example (DL)


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

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

# 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.

Tuesday, May 7, 2019

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.

AI created video look

Courtesy of DataGrid, a startup based at Japan’s Kyoto University . 
  • The poses, the clothes, the different hairstyles, stances, everything the AI just came up with it all, in a stunning bit of understanding about the humans who occupy the world around it.
  • According to a translation of the text that accompanies the video, the researchers used what’s called a Generative Adversarial Network, or GAN, to generate the high-resolution (1024×1024) images of nonexistent humans. It goes on to suggest that this kind of thing could be useful in a variety of ways, such as by creating virtual models for industries like advertising and fashion

Monday, April 29, 2019

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.


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.

Saturday, April 27, 2019

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.