Sabudh Intern's blog

Ropar data hypothesis testing After drawing inferences from the data visualization, we needed to make sure that the results are true or not for a population(we were given a sample). So, we studied  hypothesis testing and then  applied it in order to find the dependency of the factors on each other and also with the number of accidents or fatalities. Hypothesis testing  is a statistical method that is used in making statistical decisions using experimental data.  Hypothesis Testing  is basically an assumption that we make about the population parameter. Various tests used for hypothesis testing are: z-statistic and p-value t-test Chi square test ANOVA Traffic Light Application Chandigarh's traffic police gave us the responsibility of designing an android application which regulated the time that should be given to a lane's cars to pass . It was based on many factors like time of red light, total time of amber light, etc. Firstly, we made a webp

Time Series Forecasting We had sessions with our mentor Varun Goel from Tatras,Delhi throughout the week and so on different techniques used in forecasting. We covered following topics in these sessions: ● Forecasting – Introduction ● Linear Regression ● Moving Averages ● Exponential Smoothing ● ARMA, ARIMA ● Neural Networks, BRANNs What is forecasting? Forecast: (verb) to calculate or predict (some future event or condition) usually as a result of study and analysis of available pertinent data Things we forecast… – Weather parameters – Sales numbers/ Demand – Agricultural Production – Currency exchange rates, Stock prices, Gold prices, Interest rates – Internet Traffic – Loss due to Epidemics and Natural Disasters and wars Forecasting for  a Stationary Time Series A stationary time series has the form: D t = m + e t  ,  where m is a constant and e t is a random variable with mean 0 and var σ 2  Methods: ● Naive Method ● Movi

Ropar's Accident data Our company was given Ropar's Accident data by the Government to analyze and submit insights from the data that could be helpful to the government for reducing the number of serious accidents. We analyzed the data by using Tableau with the help of pie charts, histograms, etc. We also made dashboards and stories which gave us a really good insight of the data.   Convolutional Neural Networks We referred to the CMU's course on deep learning for understanding this concept.This lecture was taught by Bhiksha Raj Sir. CNNs, like neural networks, are made up of neurons with learnable weights and biases. Each neuron receives several inputs, takes a weighted sum over them, pass it through an activation function and responds with an output. Unlike neural networks, where the input is a vector, here the input is a multi-channeled image. The wholw network has a loss function. CNNs have wide applications in image and video recognition, re

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

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.

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. Examples can be Fr