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.

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.

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.


Data dimensions





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

1      2.4      -0.3

Friday, April 26, 2019

Bag of words






The Problem with Text
A problem with modeling text is that it is messy, and techniques like machine learning algorithms prefer well defined fixed-length inputs and outputs.
Machine learning algorithms cannot work with raw text directly; the text must be converted into numbers. Specifically, vectors of numbers.
In language processing, the vectors x are derived from textual data, in order to reflect various linguistic properties of the text.
This is called feature extraction or feature encoding.
A popular and simple method of feature extraction with text data is called the bag-of-words model of text.

Thursday, April 25, 2019

ERROR FUNCTION IN NN



  • In most learning networks, error is calculated as the difference between the actual output and the predicted output.
  • The error function is which tells us how far are we from the solution.
  • The function that is used to compute this error is known as loss function.
  • Different loss functions will give different errors for the same prediction and thus would have a considerable effort on the performance of the model.
EXAMPLE:
Imagine, we are standing on top of a mountain(mount Everest) and we want to descend.It is not that easy and it is cloudy and it is big and we cant see the big picture.We would look at all the possible directions where we can walk.