My book ‘Practical Machine Learning in R and Python: Second edition’ on Amazon

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 beginners and the experts in R and/or Python. Those starting their journey into datascience and ML will find the first 3 chapters useful, as they touch upon the most important programming constructs in R and Python and also deal with equivalent statements in R and Python.…
Original Post: My book ‘Practical Machine Learning in R and Python: Second edition’ on Amazon

My book “Deep Learning from first principles” now on Amazon

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 with R and Python2. Beaten by sheer pace – Cricket analytics with yorkr3. Cricket analytics with cricketr Related R-bloggers.com offers daily e-mail updates about R news and tutorials on topics such as: Data science, Big Data, R jobs, visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave,…
Original Post: My book “Deep Learning from first principles” now on Amazon

Deep Learning from first principles in Python, R and Octave – Part 8

  1. Introduction You don’t understand anything until you learn it more than one way. Marvin MinskyNo computer has ever been designed that is ever aware of what it’s doing; but most of the time, we aren’t either. Marvin MinskyA wealth of information creates a poverty of attention. Herbert Simon This post, Deep Learning from first Principles in Python, R and Octave-Part8, is my final post in my Deep Learning from first principles series. In this post, I discuss and implement a key functionality needed while building Deep Learning networks viz. ‘Gradient Checking’. Gradient Checking is an important method to check the correctness of your implementation, specifically the forward propagation and the backward propagation cycles of an implementation. In addition I also discuss some tips for tuning hyper-parameters of a Deep Learning network based on my experience. My post in this  ‘Deep…
    Original Post: Deep Learning from first principles in Python, R and Octave – Part 8

Deep Learning from first principles in Python, R and Octave – Part 3

“Once upon a time, I, Chuang Tzu, dreamt I was a butterfly, fluttering hither and thither, to all intents and purposes a butterfly. I was conscious only of following my fancies as a butterfly, and was unconscious of my individuality as a man. Suddenly, I awoke, and there I lay, myself again. Now I do not know whether I was then a man dreaming I was a butterfly, or whether I am now a butterfly dreaming that I am a man.”from The Brain: The Story of you – David Eagleman “Thought is a great big vector of neural activity”Prof Geoffrey Hinton This is the third part in my series on Deep Learning from first principles in Python, R and Octave. In the first part Deep Learning from first principles in Python, R and Octave-Part 1, I implemented logistic regression as a…
Original Post: Deep Learning from first principles in Python, R and Octave – Part 3

Deep Learning from first principles in Python, R and Octave – Part 2

“What does the world outside your head really ‘look’ like? Not only is there no color, there’s also no sound: the compression and expansion of air is picked up by the ears, and turned into electrical signals. The brain then presents these signals to us as mellifluous tones and swishes and clatters and jangles. Reality is also odorless: there’s no such thing as smell outside our brains. Molecules floating through the air bind to receptors in our nose and are interpreted as different smells by our brain. The real world is not full of rich sensory events; instead, our brains light up the world with their own sensuality.”The Brain: The Story of You” by David Eagleman “The world is Maya, illusory. The ultimate reality, the Brahman, is all-pervading and all-permeating, which is colourless, odourless, tasteless, nameless and formless“Bhagavad Gita 1.…
Original Post: Deep Learning from first principles in Python, R and Octave – Part 2

Deep Learning from first principles in Python, R and Octave – Part 1

“You don’t perceive objects as they are. You perceive them as you are.”“Your interpretation of physical objects has everything to do with the historical trajectory of your brain – and little to do with the objects themselves.”“The brain generates its own reality, even before it receives information coming in from the eyes and the other senses. This is known as the internal model” David Eagleman – The Brain: The Story of You This is the first in the series of posts, I intend to write on Deep Learning. This post is inspired by the Deep Learning Specialization by Prof Andrew Ng on Coursera and Neural Networks for Machine Learning by Prof Geoffrey Hinton also on Coursera. In this post I implement Logistic regression with a 2 layer Neural Network i.e. a Neural Network that just has an input layer and an output layer and with…
Original Post: Deep Learning from first principles in Python, R and Octave – Part 1

The 3rd paperback editions of my books on Cricket, now on Amazon

The 3rd  paperback edition of both my books on cricket is now available on Amazon for $12.99 a) Cricket analytics with cricketr, Third Edition ($12.99). This book is based on my R package ‘cricketr‘, available on CRAN and uses ESPN Cricinfo Statsguru b) Beaten by sheer pace! Cricket analytics with yorkr, 3rd edition ($12.99). This is based on my R package ‘yorkr‘ on CRAN and uses data from Cricsheet Pick up your copies today!! Note: In the 3rd edition of  the paperback book, the charts will be in black and white. If you would like the charts to be in color, please check out the 2nd edition of these books You may also like1. My book ‘Practical Machine Learning with R and Python’ on Amazon2. A crime map of India in R: Crimes against women3.  What’s up Watson? Using IBM Watson’s…
Original Post: The 3rd paperback editions of my books on Cricket, now on Amazon

My book ‘Practical Machine Learning with R and Python’ on Amazon

My book ‘Practical Machine Learning with R and Python – Machine Learning in stereo’ is now available in both paperback ($9.99) and kindle ($6.97/Rs449) versions. In this book I implement some of the most common, but important Machine Learning algorithms in R and equivalent Python code. This is almost like listening to parallel channels of music in stereo! This book is ideal both for beginners and the experts in R and/or Python. Those starting their journey into datascience and ML will find the first 3 chapters useful, as they touch upon the most important programming constructs in R and Python and also deal with equivalent statements in R and Python. Those who are expert in either of the languages, R or Python, will find the equivalent code ideal for brushing up on the other language. And finally,those who are proficient…
Original Post: My book ‘Practical Machine Learning with R and Python’ on Amazon

Practical Machine Learning with R and Python – Part 6

This is the final and concluding part of my series on ‘Practical Machine Learning with R and Python’. In this series I included the implementations of the most common Machine Learning algorithms in R and Python. The algorithms implemented were 1. Practical Machine Learning with R and Python – Part 1 In this initial post, I touch upon regression of a continuous target variable. Specifically I touch upon Univariate, Multivariate, Polynomial regression and KNN regression in both R and Python2. Practical Machine Learning with R and Python – Part 2 In this post, I discuss Logistic Regression, KNN classification and Cross Validation error for both LOOCV and K-Fold in both R and Python3. Practical Machine Learning with R and Python – Part 3 This 3rd part included feature selection in Machine Learning. Specifically I touch best fit, forward fit, backward fit, ridge(L2 regularization)…
Original Post: Practical Machine Learning with R and Python – Part 6

Practical Machine Learning with R and Python – Part 5

This is the 5th and probably penultimate part of my series on ‘Practical Machine Learning with R and Python’. The earlier parts of this series included 1. Practical Machine Learning with R and Python – Part 1 In this initial post, I touch upon univariate, multivariate, polynomial regression and KNN regression in R and Python2.Practical Machine Learning with R and Python – Part 2 In this post, I discuss Logistic Regression, KNN classification and cross validation error for both LOOCV and K-Fold in both R and Python3.Practical Machine Learning with R and Python – Part 3 This post covered ‘feature selection’ in Machine Learning. Specifically I touch best fit, forward fit, backward fit, ridge(L2 regularization) & lasso (L1 regularization). The post includes equivalent code in R and Python.4.Practical Machine Learning with R and Python – Part 4 In this part I discussed SVMs, Decision…
Original Post: Practical Machine Learning with R and Python – Part 5