Week 14 ( 01/10/2018 - 05/10/2018)

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Day 4 - Linear Regression Numpy code and Python course

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Linear Regression Numpy code   We finished coding generating data for the numpy version of Linear regression. We didn't use data from an excel sheet or a Kaggle dataset and hence we had to create our own data. For this, we created random integer data for our X and betas. Then we created a noise, as real data always has noise, using this we created Y data. the code for the same was as follows: import numpy as np samplesize=1000 num_attrs= 3 step = 0.1 x_inputs = np.random.rand(samplesize,num_attrs-1) x0 = np.ones((samplesize,1)) x_data = np.concatenate((x0, x_inputs), axis=1) noise = np.random.randn(len(x_inputs),1)  betas = np.random.rand(num_attrs,1) y_true = x_data.dot(betas) + noise  #understand this y_true.reshape(1000,1) Python course  We started an Udemy course on Python. The concepts we covered today were: Pros and Cons of Dynamic Typing String Indexing and Slicing Various String Methods String Interpolation:  ...
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Week 17 ( 22/10/18 - 26/10/18)

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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...
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Week 21 (19/11/2018 - 23/11/2018)

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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...
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