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Deep Learning from first principles in Python, R and Octave – Part 4

February 26, 2018
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Deep Learning from first principles in Python, R and Octave – Part 4

In this 4th post of my series on Deep Learning from first principles in Python, R and Octave – Part 4, I explore the details of creating a multi-class classifier using the Softmax activation unit in a neural network. The earlier posts in this series were 1. Deep Learning from first principles in Python, R … Continue reading Deep...

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

January 30, 2018
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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 … Continue reading Deep...

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

January 11, 2018
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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 … Continue reading Deep...

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

January 4, 2018
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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 … Continue reading Deep...

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

December 15, 2017
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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 … Continue reading The...

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

December 4, 2017
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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 … Continue reading My...

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Practical Machine Learning with R and Python – Part 6

November 21, 2017
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Practical Machine Learning with R and Python – Part 6

Introduction 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, … Continue reading Practical...

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Practical Machine Learning with R and Python – Part 5

November 6, 2017
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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 Python 2.Practical … Continue reading Practical...

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Practical Machine Learning with R and Python – Part 4

October 29, 2017
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Practical Machine Learning with R and Python – Part 4

This is the 4th installment of my ‘Practical Machine Learning with R and Python’ series. In this part I discuss classification with Support Vector Machines (SVMs), using both a Linear and a Radial basis kernel, and Decision Trees. Further, a closer look is taken at some of the metrics associated with binary classification, namely accuracy … Continue reading Practical...

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Practical Machine Learning with R and Python – Part 3

October 20, 2017
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Practical Machine Learning with R and Python – Part 3

In this post ‘Practical Machine Learning with R and Python – Part 3’,  I discuss ‘Feature Selection’ methods. This post is a continuation of my 2 earlier posts Practical Machine Learning with R and Python – Part 1 Practical Machine Learning with R and Python – Part 2 While applying Machine Learning techniques, the data … Continue reading Practical...

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