rameshoswal - Tumblr blog

[rameshoswal]

Types of Optimization Algos.

https://medium.com/towards-data-science/types-of-optimization-algorithms-used-in-neural-networks-and-ways-to-optimize-gradient-95ae5d39529f

[rameshoswal]

Confusing statistical terms alpha and beta

http://www.theanalysisfactor.com/confusing-statistical-terms-1-alpha-and-beta/

[rameshoswal]

K means Clustering - 10601

Clustering:

Fully Unsupervisd Algo

Automatically partition unlabeled data points  into groups of data points

Useful for

              Organizin data

              Understanding Hidden Structure

 Appliations:

              Cluster NewsArticles, Web Pages or search results by topic.

              Cluster Protein sequences by func or genes according to expression profile.

              Cluster user of Social network based on intrest(Commuity detection) - Ad target

Optimization Background:

              Coordinate Descent:

                             Goal - Minimize a function J(theta)

                                            eg. theta = argmin(J(theta))

                             Idea: Pick one dimension, and minimize along that dimension.

                                                                Algorithm:          

1.      Choosing Initial Point heta

2.      Repeat until stopping criteria is reached

a.      Theta1 = argmin J(theta1,theta2,….thetan)

b.      Theta2 = argmin J(theta1,theta2,….thetan)

c.      Theta1 = argmin J(theta1,theta2,….thetan)

.

.

d.      Thetan = argmin J(theta1,theta2,….thetan)

Note: Steps abc..,d are exact line search alo ng same axis.

                              In some cases this algo can get stuck and start oscillating along two points. In this scenario we can use something called as Block Coordinate Descent.

              Block Coordinate Descent:

                                                           Here:  An eg. With two block alpha and beta where theta = [alpha, beta]

                                                           Goal: alpha,beta = argmin J(alpha,beta) where alpha in Ra and beta in Rb

                                                                               Idea: Minimize over an entire group of variables at a time.

                                                           Algo:

1.)    Choose alpha and beta

2.)    Repeat until stopping criteria is reached

a.      Alpha – argmin (J(alpha,beta))

b.      Beta – argmin (J(alpha,beta))              

   Clustering:

Left figure - Distance between two points in same cluster is less than different cluster

Right Figure - Distance between two points in same cluster is more than different cluster

             Now we define a Object function to minimize.

What is clustering? Goal is to partition unlabeled instances into group of similar points.

Input: Unlabeld data D = {X1,X2,X3….Xn}, X(i) belongs Rm

              *We do not know the labels!

Output:

View#1: Labeled Instances {(X(1),Z(1)), (X(2),Z(2)), (X(3),Z(3))….. (X(n),Z(n))}

                                                                                         Where Z(1) E {1,2,3,….,K} k is # of clusters

View#2: Clusterings: C1,C2,…..Ck where k is number of clusters Ci = {X(i): Z(i)=j} pints in jth partition

Questions:

1.)    How many clusters are there?

2.)    How do we define similarity between points? Eg. Eucledian distance

Object based clusteting:

              Eg K-Means Objective -    

                             Input: D = {Xi}i=1N

                             Cluster Centers: {c1,c2,…,ck} = c

Decision Rule: Assign point X(i) to its nearest cluster center cj

Objective:

              C= argmin sumi=1N minj in {1,..,k}||X(i)-cj||2

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[rameshoswal]

AI Makes Stunning Photos From Your Drawings | Two Minute Papers

https://www.youtube.com/watch?v=u7kQ5lNfUfg&list=PLujxSBD-JXgnqDD1n-V30pKtp6Q886x7e&index=141

[rameshoswal]

Nice Vizualizations for Imp DS theoretical concepts

http://students.brown.edu/seeing-theory/

[rameshoswal]

Interesting gallery of Python Notebooks

https://github.com/jupyter/jupyter/wiki/A-gallery-of-interesting-Jupyter-Notebooks

[rameshoswal]

Exploratory Data Analysis

Graphical Representation used for EDA.(List is incomplete)

histogram swarmplots empirical cummulaive distribution functions

[rameshoswal]

determining if two different people are tweeting from Pres Trump’s twitter Account

http://www.datasciencecentral.com/profiles/blogs/data-science-reveals-trump-tweets-are-written-by-two-people

[rameshoswal]

What is Bayesian ML?

http://www.kdnuggets.com/2016/07/bayesian-machine-learning-explained.html

[rameshoswal]

A complete guide to data exploration

https://www.analyticsvidhya.com/blog/2016/01/guide-data-exploration/

[rameshoswal]

Steel hacks 2017, Musicon - Dynamic Song Playing based on User Activity

Recently, I along with my friends from CMU participated in Steel Hacks 2017. We build a mobile application which identifies user activity using Android Accelerator and plays song from Spotify based on activity and user preferences. 

I worked on the User Activity Recognition part where I used Android’s Google Accelerator api to receive the co-ordinates of Phone Movement. Based on this we calculated the speed with which the user is moving and categorized it into different modes like: Still, Slow Walking Brisk Walking, Jogging and Sprint.

Now Based on these different modes we pulled the Users Spotify details and played the song based on the activity and his preferences.

For more details please go to the below dev post link.

https://devpost.com/software/musicon-2zy5iq

[rameshoswal]

Steel hacks 2017, Musicon - Dynamic Song Playing based on User Activity

Recently, Me along with my friends from CMU participated in Steel Hacks 2017. We build a mobile application which identifies user activity using Android Accelerator and plays song from Spotify based on activity and user preferences. 

I worked on the User Activity Recognition part where I used Android’s Google Accelerator api to receive the co-ordinates of Phone Movement. Based on this we calculated the speed with which the user is moving and categorized it into different modes like: Still, Slow Walking Brisk Walking, Jogging and Sprint.

Now Based on these different modes we pulled the Users Spotify details and played the song based on the activity and his preferences.

For more details please go to the below dev post link.

https://devpost.com/software/musicon-2zy5iq

[rameshoswal]

https://youtu.be/fqxxv5Cyd4E?t=1037

Regression Analysis

Linear Reg Most powerful method in stat modelling. 

Applications of Regressions

Extract and exploit the relation between dependent and indep variable. 

              To talk about causal inference. What factors drive the dependent variable. So one can test hypothesis about the causal relation.

              Simple Approximation

Data set

              Independent Variable:

                             Xi = (Xi1,Xi2,….Xip)T, i = 1,2,…n

              Dependent Varibale:

                             Yi , i = 1,2,…,n

General Linear Model – For each case i, the conditional distribution is given by [yi|xi]

              Here, yi = i + εi

Where yi = β1xi,1, β2xi,2,….. βpxi,p,

β1, β2,….. βp are regression parameters

εi = residual error variable (varies over all cases)

Why to change the stock prices onto Logarithmic scale.

              Its better to show stock prices on percentage scale than abosulte value. Log gives a good idea about percentage change.

[rameshoswal]

Deep Learning for Multivariate Financial Time Series

https://www.diva-portal.org/smash/get/diva2:820891/FULLTEXT01.pdf?utm_content=bufferc5a37&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer

[rameshoswal]

Time Complexity for Different Python functions(Big Oh notation)

Source: https://wiki.python.org/moin/TimeComplexity

[rameshoswal]

Intro to clustering

https://www.analyticsvidhya.com/blog/2016/11/an-introduction-to-clustering-and-different-methods-of-clustering/

[rameshoswal]

Basic Prob every data scientist should know

https://www.analyticsvidhya.com/blog/2017/02/basic-probability-data-science-with-examples/