Wednesday, April 24, 2019

Industries to be revolutionized by artificial intelligence



Artificial intelligence (AI) and machine learning (ML) have a rapidly growing presence in today’s world, with applications ranging from heavy industry to education. From streamlining operations to informing better decision making, it has become clear that this technology has the potential to truly revolutionize how the everyday world works.

According to a panel of Forbes Technology Council members, here are 13 industries that will soon be revolutionized by AI.

1. Cybersecurity

The enterprise attack surface is massive. With its power to bring complex reasoning and self-learning in an automated fashion at massive scale, AI will be a game-changer in how we improve our cyber-resilience. - Gaurav Banga, Balbix

Monday, April 22, 2019

GAN



  • They can make entirely new image that are realistic, even they never been seen before
  • Most of the application for GANs have been images
STACKGAN 
  • Takes a textual description of the bird and than generating a high resolution of a bird matching that description.
  • These pictures have never been seen before. It is not running a image search on a database, infact GAN is drawing a probability distribution over all hypothetical images matching that description
  • We can keep running the GAN to get more images.

Tuesday, April 16, 2019

Sage Maker Services


SERVICES PROVIDED BY SAGEMAKER

1) Provides jupyter notebook instance
  • Used to explore and process data
2) API
  •  This simplifies computationally difficult task like train and deploy machine learning model

Machine Learning Workflow



Machine Learning Workflow consists of 3 components
  • Explore and process data
  • Modeling
  • Deployment
EXPLORE AND PROCESS DATA
This component consists of exploring and processing the data.

Retrieve
The first step is to retrieve the data, which includes test and train dataset. Lets take an example of housing dataset which contains csv files. We need to download the data from the source. 

Tuesday, March 12, 2019

core components of self driving cars


COMPUTER VISION:
 These are like cameras where we use camera images to figure out what the world around us look like.

SENSOR FUSION:
How we incorporate data from other sensors like lasers, radars to get richer understanding of our environment.

LOCALIZATION:
To understand where we are in the current world.

PATH PLANNING:
Chart through the world to get us where we'd like to go.

CONTROL:
How we actually turn the steering wheel and hit the throttle ,hit the break in order to execute the trajectory that we built during path planning.


Monday, February 25, 2019

Impact of scaling and shifting random variables


To make training the network easier, we standardize each of the continuous variables. That is, we'll shift and scale the variables such that they have zero mean and a standard deviation of 1.
The scaling factors are saved so we can go backwards when we use the network for predictions.

SHIFTING
If we have one random variable, that is constructed by adding a constant to another random variable
  • We would shift the mean by that constant
  • It would not shift the standard deviation

Categorical Variables


  • These are variables that fall into a category
  • There is no order for categorical variables
  • They are not quantitative variables