Books in 2016

Book reading wise, 2016 was great. Learning new things, it was fantastic. Everything else, it was so so.

Here’s the word cloud of the book names. Looks like a brain. Really nice!

Here’s the full list of the books. A mix bunch of books but unfortunately not many on investing that I would have liked to see.

Data a love story

Amy Webb

59 seconds think a little, change a lot

Richard Wiseman

The Reluctant Mr Darwin

David Quammen

Darwin among machines

George Dyson

Math Geek: From Klein Bottles to chaos theory

Rosen, Raphael 

Letters from a Father to His Son Entering College

Charles Franklin Thwing

   
   

Guns, germs and Steel

Jared Diamond

Give and Take

Adam Grant

Packing for Mars

Mary Roach

The ISIS  apocalypse

Willam McCants

Respecting truth

Lee McIntyre

Why sex is fun

Jared Diamond 

Deep work

Cal Newport 

When to Rob a Bank

Steven D. Levitt

   
   

Curious

Ian Leslie

The Making of the Fittest

Sean B. Carroll

Eating Animals

Jonathan Safran Foer

The Innovator’s Dilemma

Clayton M. Christensen

Value Investing: A Value Investor’s Journey Through The Unknown

Neely, J. Lukas

The Tell-Tale Brain

Ramachandran, V. S.

Black Box Thinking

Matthew Syed

Where Good Ideas Come From

Steven Johnson

   

The Idea Factory: Bell Labs and the Great Age of American Innovation

Gertner, Jon

The Wisest One in the Room

Thomas Gilovich and Lee Ross

Smarter Faster Better

Duhigg, Charles

I Invented the Modern Age: The Rise of Henry Ford

Richard Snow

Pebbles of Perception

Laurence Endersen

The black swan

Nicolas N Taleb

The human advantage

Suzana Herculano-Houze

Concorde

Jonathan Glancey

Food Rules

Michael Pollan

   

Made to Stick

Chip Heath

A Survival Guide to the Misinformation Age: Scientific Habits of Mind

David J. Helfand

Peak: Secrets from the New Science of Expertise

Anders Ericsson

The Everything Store: Jeff Bezos and the Age of Amazon

Stone, Brad

Brain Bugs

Dean Buonomano

The 5 Mistakes Every Investor Makes and How to Avoid Them

Peter Mallouk

Fooled by Randomness

Nassim Nicholas Taleb

A Little History of Science

William Bynum

   
   

Traffic: Why We Drive the Way We Do (And What It Says About Us)

Tom Vanderbilt

Bounce

Matthew Syed

The Halo effect

Phil Rosenzweig

Methods of Persuasion

Kolenda, Nick

Warren Buffett’s Ground Rules

Jeremy C. Miller

I Don’t

Susan Squire

   
   

Ego Is the Enemy

Ryan Holiday

How to teach quantum mechanics to your dog

Chad Orzel

THE RISE AND FALL OF THE THIRD CHIMPANZEE: EVOLUTION AND HUMAN LIFE

Jared Mason Diamond

The Making of the Atomic Bomb

Richard Rhodes

Nudge: Improving Decisions About Health, Wealth, and Happiness

Richard H. Thaler

   
   

The art of doing twice the work in half the time

Jeff Sutherland 

The Ascent of money

Niall Ferguson

Decisive

Chip and Dan Heath

The Red Queen

Matt Ridley

Creativity Inc.

Ed Catmull

   

Move your bus

Ron Clark

Concentrated investing

Allen C Belleno 

Originals

Adam Grant

   

Sapiens

Yuval Noah Harari

Do gentlemen really prefer blondes

Jena Pencott

   

The memory code

Lynne Kelly

The evolution of everything

Mat Ridley

How Not to Be Wrong : The Power of Mathematical Thinking

Jordan Ellenberg 

Bad Science

Linda Gimmerman

Eureka How inventions happen

Gavin Weightman

How memory works

Robert Madigan

From the Big Bang to Your Cells: The Remarkable Story of Minerals

Raye Kane

   

Naked money

Charles wheels

On intelligence

Jeff Hawkins

One to nine

Andrew Hodges

Influence

Robert Cialdini

   
   

Inferno

Dan Brown

Stargazers

Allan Chapman

100 baggers

Christopher Mayer

What technology wants

Kevin Kelly

Homo Deus

Yuval Noah Harari

 

 

What are you favourite data science books?

think_bayes

Recently someone on my the blog asked me about some book ideas on data science and today I saw this post on ebooks available on data science, so here you go.

Please visit the link for the freely available ebooks. http://blog.paralleldots.com/data-scientist/list-must-read-books-data-science/

A good shout out for the good folks at parralledots. (I like the name!!)

My pick, definitely the think Bayes, and think stats. What are your favourite data science books?

 

Machine learning: Thou aimest high.

Was reading the book Why Nation fails by by Daron Acemoglu and found this anecdote. This reminded me of similar thoughts, many have advocated about artificial intelligence and machine learning.

In 1583 William Lee returned from his studies at the University of Cambridge to become the local priest in Calverton England. Elizabeth I (1558–1603) had recently issued a ruling that her people should always wear a knitted cap. Lee recorded that “knitters were the only means of producing such garments but it took so long to finish the article. I began to think. I watched my mother and my sisters sitting in the evening twilight plying their needles. If garments were made by two needles and one line of thread, why not several needles to take up the thread.”

This momentous thought was the beginning of the mechanization of textile production. Lee became obsessed with making a machine that would free people from endless hand-knitting. He recalled, “My duties to Church and family I began to neglect. The idea of my machine and the creating of it ate into my heart and brain.” Finally, in 1589, his “stocking frame” knitting machine was ready. He travelled to London with excitement to seek an interview with Elizabeth I to show her how useful the machine would be and to ask her for a patent that would stop other people from copying the design.

He rented a building to set the machine up and, with the help of his local member of Parliament Richard Parkyns, met Henry Carey, Lord Hundson, a member of the Queen’s Privy Council. Carey arranged for Queen Elizabeth to come see the machine, but her reaction was devastating. She refused to grant Lee a patent, instead observing, “Thou aimest high, Master Lee. Consider thou what the invention could do to my poor subjects. It would assuredly bring to them ruin by depriving them of employment, thus making them beggars.”

Crushed, Lee moved to France to try his luck there; when he failed there, too, he returned to England, where he asked James I (1603–1625), Elizabeth’s successor, for a patent. James I also refused, on the same grounds as Elizabeth. Both feared that the mechanization of stocking production would be politically destabilizing. It would throw people out of work, create unemployment and political instability, and threaten royal power. The stocking frame was an innovation that promised huge productivity increases, but it also promised creative destruction.

I am not smart enough to know if the apprehensions are right or wrong, but as a machine learning enthusiast, I am fascinated with the field. 

Deep learning, AI, ML are tools like knife and hammer that we are now beginning to understand better and put them to practical use.
Exciting times.

Switch from Discrete Mathematics to Probability, Statistics

From the book Foundations of Data Science by John Hopcroft and Ravindran Kannan

Computer science as an academic discipline began in the 60’s. Emphasis was on programming languages, compilers, operating systems, and the mathematical theory that supported these areas. Courses in theoretical computer science covered finite automata,regular expressions, context free languages, and computability.

In the 70’s, algorithms was added as an important component of theory. The emphasis was on making computers useful. Today, a fundamental change is taking place and the focus is more on applications.

There are many reasons for this change. The merging of computing and communications has played an important role. The enhanced ability to observe, collect and store data in the natural sciences, in commerce, and in other fields calls for a change in our understanding of data and how to handle it in the modern setting. The emergence of the web and social networks, which are by far the largest such structures, presents both opportunities and challenges for theory

All this entails, there is the switch from discrete mathematics to more emphasis on probability and statistics.

Amazing Machine Learning in 1950!! 

Watch this excellent videos from the 1950’s that demonstrates machine learning. Amazing!

Learnt about this from the book on Bell Labs that’s one of my top recommended read for anyone.

If you are interested in Machine Learning, don’t miss this other video on Algorithms and Techniques that are changing our world

 

 

 

From birth of transistor to tell tale brain


In the last post I listed my reading list for the year. Someone asked, which books will I recommend from this stack?
The books that come to the mind are…

1. The bell labs

Engrossing account of the birth of transistors and otheri bell lab inventions. Loved the way how the chapters were framed and how each was organized. 

2. Gun germs and steel

Long book but fascinating in many ways. Takes you back in time and unfolds the history bit by bit. Highly recommended. 

3. The tell tale brain

Another fascinating book. I thought I knew enough about the brain and this book explained everything I knew with a neurological twist. Loved it. After reading this you will never see brain with the same eyes. 

4. Survival of the fittest

Evolution from a genes perspective. Loved it and many a times, turned to YouTube to experience and learn more. 

5. Black swan

This is again a long book but fascinating. Read it for the longest time and still marinating on some of it idea. Cryptic language but love the way the author was playing with the reader throughout the books narrative. 
6. I built the Industrial Age Henry Ford

Started the book thinking I know the crux of the topic but found lot of new perspective. The author transforms the reader to a time when cars were just introduced. It reminds me of the digital and mobile transformation that is happening now. Loved the entire book although felt the organisation of chapters was a bit off for me.

Do you have any recommendations?