updating profile details

_config.yml

@@ -1,6 +1,18 @@
-title: Minimal theme
-logo: /assets/img/logo.png
-description: Minimal is a theme for GitHub Pages.
+title: Shubham Rai
+logo: /assets/img/Image20231122005459.png
+description:
+  <p align="center">
+  <a href="pdf/resume.pdf">Resume</a> |
+  <a href="https://www.linkedin.com/in/rai-shubham/">LinkedIn</a> |
+  <a href="https://github.com/rai-shubh">GitHub</a>
+
+  <br>   
+  I'm working on Data Science, Big Data, Spark, Machine learning & NLP related problems.
+  Also handing the Big Data from real-time Ad call data.
+  Worked with IBM Research at Big Data Analytics team.
+  Working in AdTech domain for 3 years.
+  <br>
+  </p>
 show_downloads: true
 google_analytics:
 theme: jekyll-theme-minimal

index.md

@@ -1,118 +1,65 @@
+# Data Science Portfolio
 ---
-layout: default
----
-
-Text can be **bold**, _italic_, or ~~strikethrough~~.
-
-[Link to another page](./another-page.html).
-
-There should be whitespace between paragraphs.
-
-There should be whitespace between paragraphs. We recommend including a README, or a file with information about your project.
+## Machine learning
 
-# Header 1
+### Fraud Detection
 
-This is a normal paragraph following a header. GitHub is a code hosting platform for version control and collaboration. It lets you and others work together on projects from anywhere.
+Fraud detection is a set of processes and analyses that allow businesses to identify and prevent unauthorized financial activity. This can include fraudulent credit card transactions, identify theft, cyber hacking, insurance scams, and more.
 
-## Header 2
+[![View on GitHub](https://img.shields.io/badge/GitHub-View_on_GitHub-blue?logo=GitHub)](https://github.com/rai-shubh/fraud_detection.git)
 
-> This is a blockquote following a header.
->
-> When something is important enough, you do it even if the odds are not in your favor.
-
-### Header 3
-
-```js
-// Javascript code with syntax highlighting.
-var fun = function lang(l) {
-  dateformat.i18n = require('./lang/' + l)
-  return true;
-}
-```
-
-```ruby
-# Ruby code with syntax highlighting
-GitHubPages::Dependencies.gems.each do |gem, version|
-  s.add_dependency(gem, "= #{version}")
-end
-```
+<center><img src="images/fraud_detection.jpg"/></center>
 
-#### Header 4
-
-*   This is an unordered list following a header.
-*   This is an unordered list following a header.
-*   This is an unordered list following a header.
+---
+### Financial-Models-Numerical-Methods
 
-##### Header 5
+This is just a collection of topics and algorithms that in my opinion are interesting.
 
-1.  This is an ordered list following a header.
-2.  This is an ordered list following a header.
-3.  This is an ordered list following a header.
+It contains several topics that are not so popular nowadays, but that can be very powerful. Usually, topics such as PDE methods, Lévy processes, Fourier methods or Kalman filter are not very popular among practitioners, who prefers to work with more standard tools.
+The aim of these notebooks is to present these interesting topics, by showing their practical application through an interactive python implementation.
 
-###### Header 6
+---
+### LSTM Neural Network for Time Series Prediction
 
-| head1        | head two          | three |
-|:-------------|:------------------|:------|
-| ok           | good swedish fish | nice  |
-| out of stock | good and plenty   | nice  |
-| ok           | good `oreos`      | hmm   |
-| ok           | good `zoute` drop | yumm  |
+The following article sections will briefly touch on LSTM neuron cells, give a toy example of predicting a sine wave then walk through the application to a stochastic time series. The article assumes a basic working knowledge of simple deep neural networks.
 
-### There's a horizontal rule below this.
+---
+### Text Classification
 
-* * *
+It is slightly simplified implementation of Kim's Convolutional Neural Networks for Sentence Classification paper in Tensorflow.
 
-### Here is an unordered list:
+---
+### Awesome Machine Learning
 
-*   Item foo
-*   Item bar
-*   Item baz
-*   Item zip
+A curated list of awesome machine learning frameworks, libraries and software (by language). Inspired by awesome-php.
 
-### And an ordered list:
+---
+### Gesture Recognizer
 
-1.  Item one
-1.  Item two
-1.  Item three
-1.  Item four
+Gesture recognition via CNN neural network implemented in Keras + Theano + OpenCV
 
-### And a nested list:
+Key Requirements: Python 3.6.1 OpenCV 3.4.1 Keras 2.0.2 Tensorflow 1.2.1 Theano 0.9.0 (obsolete and not supported any further)
 
-- level 1 item
-  - level 2 item
-  - level 2 item
-    - level 3 item
-    - level 3 item
-- level 1 item
-  - level 2 item
-  - level 2 item
-  - level 2 item
-- level 1 item
-  - level 2 item
-  - level 2 item
-- level 1 item
+Suggestion: Better to download Anaconda as it will take care of most of the other packages and easier to setup a virtual workspace to work with multiple versions of key packages like python, opencv etc.
 
-### Small image
+---
+### Human Activity Recognition
 
-![Octocat](https://github.githubassets.com/images/icons/emoji/octocat.png)
+Human Activity Recognition (HAR) using smartphones dataset and an LSTM RNN. Classifying the type of movement amongst six categories:
 
-### Large image
+<dd>WALKING,</dd>
+<dd>WALKING_UPSTAIRS,</dd>
+<dd>WALKING_DOWNSTAIRS,</dd>
+<dd>SITTING,</dd>
+<dd>STANDING,</dd>
+<dd>LAYING.</dd>
+</dl>
 
-![Branching](https://guides.github.com/activities/hello-world/branching.png)
+Compared to a classical approach, using a Recurrent Neural Networks (RNN) with Long Short-Term Memory cells (LSTMs) require no or almost no feature engineering. Data can be fed directly into the neural network who acts like a black box, modeling the problem correctly. Other research on the activity recognition dataset can use a big amount of feature engineering, which is rather a signal processing approach combined with classical data science techniques. The approach here is rather very simple in terms of how much was the data preprocessed.
 
+Let's use Google's neat Deep Learning library, TensorFlow, demonstrating the usage of an LSTM, a type of Artificial Neural Network that can process sequential data / time series.
 
-### Definition lists can be used with HTML syntax.
 
-<dl>
-<dt>Name</dt>
-<dd>Godzilla</dd>
-<dt>Born</dt>
-<dd>1952</dd>
-<dt>Birthplace</dt>
-<dd>Japan</dd>
-<dt>Color</dt>
-<dd>Green</dd>
-</dl>
 
 ```
 Long, single-line code blocks should not wrap. They should horizontally scroll if they are too long. This line should be long enough to demonstrate this.