week 15(18-22 November)

In this week we  started trying our hands on Deep Learning assignment part 2 "Speech Recognition".
In this challenge we will take our knowledge of feedforward neural networks and apply it to a more useful task than recognizing handwritten digits: speech recognition. We were provided a dataset of audio recordings (utterances) and their phoneme state (subphoneme) labels. The data comes from articles published in the Wall Street Journal (WSJ) that are read aloud and labelled using the original text. It is crucial for us to have a means to distiguish different sounds in speech that may or may not represent the same letter or combinations of letters in the written alphabet. For example, the words "jet" and "ridge" both contain the same sound and we refer to this elemental sound as the phoneme "JH". For this challenge we will consider 46 phonemes in the english language.

Next we had a session with Danko sir and we did open discussion with him on human intelligence and cleared vision on too what extent machines can acquire intelligence.We solved then Sehra sir's next assignment on hypothesis testing using R.The function t.test is available in R for performing t-tests.

 
> x = rnorm(10)
> y = rnorm(10)
> t.test(x,y)

        Welch Two Sample t-test

data:  x and y
t = 1.4896, df = 15.481, p-value = 0.1564
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -0.3221869  1.8310421
sample estimates:
 mean of x  mean of y
 0.1944866 -0.5599410

Comments

Post a Comment

Popular posts from this blog

week 14(11 -15 November)

Image
In this week , we continued with deep learning course lecture no.2 "The neural net as a universal approximator" which includes recap of previous lecture related to perceptron and its firing condition.After that we first learned about deep layer structures,then we move on to multilayer perceptrons and how it is used to evaluate boolean expression , learning geometrical shapes and also learnt about required optimal depth for a neural network. Another task was given to us by Sehra sir i.e. hypothesis testing and z-statistics assignment using R.There were 3-4 assignments which was supposed to be completed in 3 days. We also watched Danko sir's lecture no.2 .The lecture was about first understandig human level intelligence and how can we achieve that in computers.He taught us about electic models and specialized models also.
Read more

week 17(2-6 December)

Lecture - Design Thinking This week we had a session with Mandeep Singh Kwatria on Design Thinking to help thinking about good innovative ideas. Design Thinking is a design methodology that provides a solution-based approach to solving problems. It’s extremely useful in tackling complex problems that are ill-defined or unknown, by understanding the human needs involved, by re-framing the problem in human-centric ways, by creating many ideas in brainstorming sessions, and by adopting a hands-on approach in prototyping and testing. The three Design Thinking Phases are: Inspire Ideate Implement   A ground-breaking innovation will be desirable, feasible and viable. Paddy Crop Project Along with other work, we also had a project from one of our professors related to paddy (confidential) which we worked on using the knowledge we got in this internship.
Read more

week 13( 4-8 November)

Image
Statistics By Sehra Sir We had been having sessions with Sukhjit Sehra Sir on statistics since the past week. This week we learned about the following topics: Probability and Probability Distributions Probability theory developed from the study of games of chance like dice and cards.  A process like flipping a coin, rolling a die or drawing a card from a deck is called probability experiments.  An outcome is a specific result of a single trial of a probability experiment. Probability Distributions Probability theory is the foundation for statistical inference.  A probability distribution is a device for indicating the values that a random variable may have.  There are two categories of random variables.  These are discrete random variables and continuous random variables. Discrete random variable The probability distribution of a discrete random variable specifies all possible values of a discrete random variable along with their respective probabilities. Example
Read more