Author: Tinniam V Ganesh

Feed: R-bloggers. Author: Tinniam V Ganesh. Looking at the cumulative average runs we can see a gradual drop in the cumulative average for Tendulkar while Kohli and Gavaskar’s performance seems to be getting better 13. Cumulative average strike rate of batsmen Tendulkar’s strike rate is better than Kohli and Gavaskar par(mfrow=c(2,2)) par(mar=c(4,4,2,2)) batsmanCumulativeStrikeRate("./tendulkar.csv","Tendulkar") batsmanCumulativeStrikeRate("./kohli.csv","Kohli") batsmanCumulativeStrikeRate("./gavaskar.csv","Gavaskar") 14 Performance forecast of batsmen The forecasted performance for Kohli and Gavaskar is higher than that of Tendulkar par(mfrow=c(2,2)) par(mar=c(4,4,2,2)) batsmanPerfForecast("./tendulkar.csv","Sachin Tendulkar") batsmanPerfForecast("./kohli.csv","Kohli") batsmanPerfForecast("./gavaskar.csv","Gavaskar") 15. Relative strike rate of batsmen par(mar=c(4,4,2,2)) frames "./tendulkar.csv","./kohli.csv","gavaskar.csv") names "Tendulkar","Kohli","Gavaskar") relativeBatsmanSR(frames,names) 16. Relative Runs frequency of batsmen par(mar=c(4,4,2,2)) frames "./tendulkar.csv","./kohli.csv","gavaskar.csv") ... Read More

Feed: R-bloggers. Author: Tinniam V Ganesh. “Lights, camera and … action – Take 4+!” This post includes  a rework of all presentation of ‘Elements of Neural Networks and Deep  Learning Parts 1-8 ‘ since my earlier presentations had some missing parts, omissions and some occasional errors. So I have re-recorded all the presentations.This series of presentation will do a deep-dive  into Deep Learning networks starting from the fundamentals. The equations required for performing learning in a L-layer Deep Learning network  are derived in detail, starting from the basics. Further, the presentations also discuss multi-class classification, regularization techniques, and gradient descent optimization methods in ... Read More

Feed: R-bloggers. Author: Tinniam V Ganesh. This is the final set of presentations in my series ‘Elements of Neural Networks and Deep Learning’. This set follows the earlier 2 sets of presentations namely1. My presentations on ‘Elements of Neural Networks & Deep Learning’ -Part1,2,32. My presentations on ‘Elements of Neural Networks & Deep Learning’ -Parts 4,5 In this final set of presentations I discuss initialization methods, regularization techniques including dropout. Next I also discuss gradient descent optimization methods like momentum, rmsprop, adam etc. Lastly, I briefly also touch on hyper-parameter tuning approaches. The corresponding implementations are available in vectorized R, Python ... Read More

Feed: R-bloggers. Author: Tinniam V Ganesh. I will be uploading a series of presentations on ‘Elements of Neural Networks and Deep Learning’. In these video presentations I discuss the derivations of L -Layer Deep Learning Networks, starting from the basics. The corresponding implementations are available in vectorized R, Python and Octave are available in my book ‘Deep Learning from first principles:Second edition- In vectorized Python, R and Octave‘ 1. Elements of Neural Networks and Deep Learning – Part 1This presentation introduces Neural Networks and Deep Learning. A look at history of Neural Networks, Perceptrons and why Deep Learning networks are ... Read More

Feed: R-bloggers. Author: Tinniam V Ganesh. Are you wondering whether to get into the ‘R’ bus or ‘Python’ bus?My suggestion is to you is “Why not get into the ‘R and Python’ train?” The third edition of my book ‘Practical Machine Learning with R and Python – Machine Learning in stereo’ is now available in both paperback ($12.99) and kindle ($8.99/Rs449) versions.  In the third edition all code sections have been re-formatted to use the fixed width font ‘Consolas’. This neatly organizes output which have columns like confusion matrix, dataframes etc to be columnar, making the code more readable.  There is a science to ... Read More

Feed: R-bloggers. Author: Tinniam V Ganesh. The second edition of my book ‘Deep Learning from first principles:Second Edition- In vectorized Python, R and Octave’, is now available on Amazon, in both paperback ($14.99)  and kindle ($9.99/Rs449/-)  versions. Since this book is almost 70% code, all functions, and code snippets have been formatted to use the fixed-width font ‘Lucida Console’. In addition line numbers have been added to all code snippets. This makes the code more organized and much more readable. I have also fixed typos in the book The book includes the following chapters Table of Contents Preface 4 Introduction 6 ... Read More

Feed: R-bloggers. Author: Tinniam V Ganesh. The plot is a scatter plot of Runs vs Balls faced and Minutes at Crease. A prediction plane is fitted 21. Predicting Runs given Balls Faced and Minutes at Crease A multi-variate regression plane is fitted between Runs and Balls faced +Minutes at crease. import cricpy.analytics as ca import numpy as np import pandas as pd BF = np.linspace( 10, 400,15) Mins = np.linspace( 30,600,15) newDF= pd.DataFrame({'BF':BF,'Mins':Mins}) dravid = ca.batsmanRunsPredict("../dravid.csv",newDF,"Dravid") print(dravid) ## BF Mins Runs ## 0 10.000000 30.000000 0.519667 ## 1 37.857143 70.714286 13.821794 ## 2 65.714286 111.428571 27.123920 ## 3 93.571429 152.142857 ... Read More

Feed: R-bloggers. Author: Tinniam V Ganesh. sparkR.session() tendulkar1 read.df("/FileStore/tables/tendulkar.csv", header = "true", delimiter = ",", source = "csv", inferSchema = "true", na.strings = "") print(dim(tendulkar1)) tendulkar1 SparkR::filter(tendulkar1,tendulkar1$Runs != "DNB") print(dim(tendulkar1)) tendulkar1SparkR::filter(tendulkar1,tendulkar1$Runs != "TDNB") print(dim(tendulkar1)) tendulkar1SparkR::filter(tendulkar1,tendulkar1$Runs != "absent") print(dim(tendulkar1)) withColumn(tendulkar1, "Runs", cast(tendulkar1$Runs, "double")) head(SparkR::distinct(tendulkar1[,"Runs"]),20) tendulkar1$Runs=SparkR::regexp_replace(tendulkar1$Runs, "\*", "") head(SparkR::distinct(tendulkar1[,"Runs"]),20) df=SparkR::summarize(SparkR::groupBy(tendulkar1, tendulkar1$Ground), mean = mean(tendulkar1$Runs), minRuns=min(tendulkar1$Runs),maxRuns=max(tendulkar1$Runs)) head(df,20) ... Read More

Feed: R-bloggers. Author: Tinniam V Ganesh. The second edition of my book ‘Practical Machine Learning with R and Python – Machine Learning in stereo’ is now available in both paperback ($10.99) and kindle ($7.99/Rs449) versions.  This second edition includes more content,  extensive comments and formatting for better readability. In this book I implement some of the most common, but important Machine Learning algorithms in R and equivalent Python code.1. Practical machine with R and Python: Second Edition – Machine Learning in Stereo(Paperback-$10.99)2. Practical machine with R and Python Second Edition – Machine Learning in Stereo(Kindle- $7.99/Rs449) This book is ideal both for ... Read More

Feed: R-bloggers. Author: Tinniam V Ganesh. My 4th book(self-published), “Deep Learning from first principles – In vectorized Python, R and Octave” (557 pages), is now available on Amazon in both paperback ($16.99) and kindle ($6.65/Rs449). The book starts with the most primitive 2-layer Neural Network and works  its way to a generic L-layer Deep Learning Network, with all the bells and whistles.  The book includes detailed derivations and vectorized implementations in Python, R and Octave.  The code has been extensively  commented and has been included in the Appendix section. Pick up your copy today!!! My other books1. Practical Machine Learning ... Read More