Week 8 ( 20/08/2018 - 24/08/2018)

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Day 1 - Numpy Revision

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Numpy Revision  We made a python program for the following random walk question: Say you are standing at the bottom of a staircase with a dice. With each throw of the dice, you either move down one step (if you get a 1 or 2 on the dice) or move up one step (if you get a 3, 4 or 5 on the dice). If you throw a 6 on the dice, you throw the dice again and move up the staircase by the number you get on that second throw. Note if you are on the base of the staircase you cannot move down! What is the probability that you will reach more than 60 steps after 250 throws of the dice? This question required the knowledge of np.random.seed. It s eeds the generator.  It makes the random numbers predictable. When the value is reset, the same numbers will appear every time. If a  seed is  not assigned , NumPy automatically selects a random seed value based on the system's random number generator device or on the clock The code was as follows: import numpy as np n...
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Week 15 (8/10/18 - 12/10/18)

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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 probabiliti...
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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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