Code With Kal

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 rec

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

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. Example

Anomaly Detection    Session with Satnam Singh sir  All the interns had a 2 hour long session with Dr.  Satnam Singh ,chief data scientist, Acalvio technologies, Bengaluru, India. In the session, he discussed various points and issues related to cyber security and online frauds and he shared some domain knowledge on the related topics and his team's work. It was an interactive session and he also asked about the work interns were doing. He shared his experience which benefited students and we learned some new approaches and terminology. Problem statement Satnam sir shared a  kaggle problem  and asked all of us to work on it. It was a credit card fraud detection problem and was to be solved as an anomaly detection problem with statistical way without using any libraries such as scikit-learn etc. Sir gave us ample amount of time to work on it before he would review all our progress and code. So after the session, we started exploring different ways to appro

After completing my news article recommender last week the upcoming week brought me opportunity to explore a library for high dimensional space visualization t-SNE , I was told by my mentor Mr.Vikram Jha to explore it and tell insights. t-SNE t-Distributed Stochastic Neighbor Embedding (t-SNE) is a ( prize-winning ) technique for dimensionality reduction that is particularly well suited for the visualization of high-dimensional datasets. Before jumping to t-SNE i knew about old technique of dimensionality reduction that is PCA,  Principal Component Analysis, I first studied in ISB videos but when Sarabjot sir explained,it  became thorough PCA Principal component analysis (PCA) is a technique used to emphasize variation and bring out strong patterns in a dataset. It's often used to make data easy to explore and visualize.