10 books like Hello World

By Hannah Fry,

Here are 10 books that authors have personally recommended if you like Hello World. Shepherd is a community of 7,000+ authors sharing their favorite books with the world.

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The Art of Statistics

By David Spiegelhalter,

Book cover of The Art of Statistics: How to Learn from Data

What if you are faced with a problem for which a standard approach doesn’t yet exist? In such a case, you will need to be able to figure out the approach from the first principles. This book will help you learn how to derive insights starting from raw data.

The Art of Statistics

By David Spiegelhalter,

Why should I read it?

2 authors picked The Art of Statistics as one of their favorite books, and they share why you should read it.

What is this book about?

'A statistical national treasure' Jeremy Vine, BBC Radio 2

'Required reading for all politicians, journalists, medics and anyone who tries to influence people (or is influenced) by statistics. A tour de force' Popular Science

Do busier hospitals have higher survival rates? How many trees are there on the planet? Why do old men have big ears? David Spiegelhalter reveals the answers to these and many other questions - questions that can only be addressed using statistical science.

Statistics has played a leading role in our scientific understanding of the world for centuries, yet we are all familiar with the way…


Invisible Women

By Caroline Criado Perez,

Book cover of Invisible Women: Data Bias in a World Designed for Men

This book challenged me greatly on a personal and professional level. Invisible Women reveals how in a world built for and by men we are systematically ignoring half of the population, often with disastrous consequences. It has made me consciously act within our business to ensure we are engaging other perspectives in our decision-making processes. Looking beyond what society, media, and advertising want us to see – Perez encourages us to evaluate how we personally can choose to either perpetuate or work towards a society that’s more equal and that sets things up to bring balance into an unbalanced world.

Invisible Women

By Caroline Criado Perez,

Why should I read it?

5 authors picked Invisible Women as one of their favorite books, and they share why you should read it.

What is this book about?

Winner of the 2019 Financial Times and McKinsey Business Book of the Year Award
Winner of the 2019 Royal Society Science Book Prize

Data is fundamental to the modern world. From economic development, to healthcare, to education and public policy, we rely on numbers to allocate resources and make crucial decisions. But because so much data fails to take into account gender, because it treats men as the default and women as atypical, bias and discrimination are baked into our systems. And women pay tremendous costs for this bias, in time, money, and often with their lives.

Celebrated feminist advocate…


Factfulness

By Hans Rosling, Anna Rosling Rönnlund, Ola Rosling

Book cover of Factfulness: Ten Reasons We're Wrong about the World--And Why Things Are Better Than You Think

For many, the most mysterious thing about science is the way that it relies on mathematics. Many find the way that numbers are used and presented impenetrable. Yet in this wonderful book, the late Hans Rosling shows just how and why our biases make it so difficult for us to put realistic numbers to what’s happening in the world around us. All around the world, people were asked questions about the state of the world and consistently their answers were worse than choosing at random—because we almost always think things are far worse than they really are. Rosling uncovers the real numbers and presents them in an impressively easy-to-absorb way.

Factfulness

By Hans Rosling, Anna Rosling Rönnlund, Ola Rosling

Why should I read it?

4 authors picked Factfulness as one of their favorite books, and they share why you should read it.

What is this book about?

'A hopeful book about the potential for human progress when we work off facts rather than our inherent biases.' BARACK OBAMA

'One of the most important books I've ever read - an indispensable guide to thinking clearly about the world.' BILL GATES

*#1 Sunday Times bestseller * New York Times bestseller * Observer 'best brainy book of the decade' * Irish Times bestseller * Guardian bestseller * audiobook bestseller *

Factfulness: The stress-reducing habit of only carrying opinions for which you have strong supporting facts.

When asked simple questions about global trends - why the world's population is increasing; how…


The Numbers Game

By Andrew Dilnot, Michael Blastland,

Book cover of The Numbers Game: The Commonsense Guide to Understanding Numbers in the News, in Politics, and in Life

I should declare an interest here: I present a BBC Radio show that Blastland and Dilnot created. This book was effectively my “how to” manual on the way into the studio that they had vacated. It’s a wise and varied guide to the power and the pitfalls of data, poetically written and full of subtle wisdoms.

The Numbers Game

By Andrew Dilnot, Michael Blastland,

Why should I read it?

1 author picked The Numbers Game as one of their favorite books, and they share why you should read it.

What is this book about?

The Strunk and White of statistics team up to help the average person navigate the numbers in the news

Drawing on their hugely popular BBC Radio 4 show More or Less, journalist Michael Blastland and internationally known economist Andrew Dilnot delight, amuse, and convert American mathphobes by showing how our everyday experiences make sense of numbers.

The radical premise of The Numbers Game is to show how much we already know and give practical ways to use our knowledge to become cannier consumers of the media. If you've ever wondered what "average" really means, whether the scare stories about cancer…


The Master Algorithm

By Pedro Domingos,

Book cover of The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World

This book provides an excellent description of the various kinds of machine learning approaches and asks the question of whether there will be a Master Algorithm, one single (universal) algorithm that learns all kinds of tasks from data. The author, Pedro Domingos, introduces the different approaches to building intelligence and the research tribes exploring them – Symbolists (with its foundations in Philosophy and Logic), Connectionists (foundations in Neuro/Cognitive Science), Evolutionaries (foundations in Evolutionary Biology), Bayesians (statistical foundations), and Analogizers (Psychology). He also introduces some of his own ideas on what the master machine learning algorithm might look like. It’s a really useful primer for those who are not deeply immersed in machine learning but it’s written for readers with at least a basic engineering and computer science background.

The Master Algorithm

By Pedro Domingos,

Why should I read it?

1 author picked The Master Algorithm as one of their favorite books, and they share why you should read it.

What is this book about?

Algorithms increasingly run our lives. They find books, movies, jobs, and dates for us, manage our investments, and discover new drugs. More and more, these algorithms work by learning from the trails of data we leave in our newly digital world. Like curious children, they observe us, imitate, and experiment. And in the world's top research labs and universities, the race is on to invent the ultimate learning algorithm: one capable of discovering any knowledge from data, and doing anything we want, before we even ask.Machine learning is the automation of discovery,the scientific method on steroids,that enables intelligent robots and…


Programming Collective Intelligence

By Toby Segaran,

Book cover of Programming Collective Intelligence: Building Smart Web 2.0 Applications

This was my favorite book when I started my career. It talks about how information is processed, in an intelligent way, in the internet age. It acts as a tutorial to teach developers how to code our own ML programs, from online dating services, to document analyzer, and search engine. The author did an excellent job of explaining abstract ML algorithms with clear examples. His coding style in Python reads clearly, which makes the book more beginner-friendly.

Don’t get disappointed when you know this book is more than a decade old. It was a visionary book back in the day and it is still relevant today.

Programming Collective Intelligence

By Toby Segaran,

Why should I read it?

1 author picked Programming Collective Intelligence as one of their favorite books, and they share why you should read it.

What is this book about?

Want to tap the power behind search rankings, product recommendations, social bookmarking, and online matchmaking? This fascinating book demonstrates how you can build Web 2.0 applications to mine the enormous amount of data created by people on the Internet. With the sophisticated algorithms in this book, you can write smart programs to access interesting datasets from other web sites, collect data from users of your own applications, and analyze and understand the data once you've found it. Programming Collective Intelligence takes you into the world of machine learning and statistics, and explains how to draw conclusions about user experience, marketing,…


Introduction to Algorithms

By Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, Clifford Stein

Book cover of Introduction to Algorithms

With over one million copies sold this may be the most popular computer science book in the world. This bedrock of computer science education is both a definitive textbook and reference book and is a must-have for anyone in the field of computer science. This latest edition is significantly updated and includes color throughout the text.

Introduction to Algorithms

By Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, Clifford Stein

Why should I read it?

1 author picked Introduction to Algorithms as one of their favorite books, and they share why you should read it.


Machine Learning For Absolute Beginners

By Oliver Theobald,

Book cover of Machine Learning For Absolute Beginners: A Plain English Introduction

This could be the first stop of your brand new machine learning journey. I personally like how the technical concept is translated into plain English – each chapter starts with a high-level overview of a ML algorithm or methodology, concise and clear, followed by lots of visual examples and real world scenarios. I can guarantee you won’t get lost halfway. The book focuses on getting you introduced to ML with minimal math. But if you want to grasp some more of math, the next book I recommend is waiting for you. 

Machine Learning For Absolute Beginners

By Oliver Theobald,

Why should I read it?

1 author picked Machine Learning For Absolute Beginners as one of their favorite books, and they share why you should read it.

What is this book about?

NOTICE: To buy the newest edition of this book (2021), please search "Machine Learning Absolute Beginners Third Edition" on Amazon. The product page you are currently viewing is for the 2nd Edition (2017) of this book.

Featured by Tableau as the first of "7 Books About Machine Learning for Beginners."

Ready to spin up a virtual GPU instance and smash through petabytes of data? Want to add 'Machine Learning' to your LinkedIn profile?

Well, hold on there...

Before you embark on your epic journey, there are some high-level theory and statistical principles to weave through first.
But rather than spend…


Discriminating Data

By Wendy Hui Kyong Chun, Alex Barnett (illustrator),

Book cover of Discriminating Data: Correlation, Neighborhoods, and the New Politics of Recognition

Digital technology, like technology generally, is commonly assumed to be value neutral. Wendy Chun reveals that structurally embedded in digital operating systems and data collection are values that reproduce and extend existing modes of discriminating while also originating new ones. In prompting and promoting the grouping together of people who are alike—in habits, culture, looks, and preferences—the logic of the algorithm reproduces and amplifies discriminatory trends. Chun reveals how the logics of the digital reinforce the restructuring of racism by the neoliberal turn that my own book lays out.

Discriminating Data

By Wendy Hui Kyong Chun, Alex Barnett (illustrator),

Why should I read it?

1 author picked Discriminating Data as one of their favorite books, and they share why you should read it.

What is this book about?

How big data and machine learning encode discrimination and create agitated clusters of comforting rage.

In Discriminating Data, Wendy Hui Kyong Chun reveals how polarization is a goal—not an error—within big data and machine learning. These methods, she argues, encode segregation, eugenics, and identity politics through their default assumptions and conditions. Correlation, which grounds big data’s predictive potential, stems from twentieth-century eugenic attempts to “breed” a better future. Recommender systems foster angry clusters of sameness through homophily. Users are “trained” to become authentically predictable via a politics and technology of recognition. Machine learning and data analytics thus seek to disrupt…


The Deep Learning Revolution

By Terrence J. Sejnowski,

Book cover of The Deep Learning Revolution

The other books in this series are mostly about the real brain. But artificial intelligence promises us a new enhanced brain. What does the future hold? Terrence Sejnowski is a neuroscientist who was one of the first to realize the potential of AI. Since he has been there from the start, in this book he gives the reader an exciting inside story on the people and the advances that are reshaping our lives.

Early attempts at AI were limited, but once computational power took off big computers running multilayer neural nets began proving that they could defeat humans at the most demanding games, enhance human capabilities such as pattern recognition, text recognition, language translation, and driverless vehicles, and work to obtain rewards, just like a human. While these advances are dramatic, it is well to remember that the networks are built not from representations of real neurons, but rather from…

The Deep Learning Revolution

By Terrence J. Sejnowski,

Why should I read it?

1 author picked The Deep Learning Revolution as one of their favorite books, and they share why you should read it.

What is this book about?

How deep learning—from Google Translate to driverless cars to personal cognitive assistants—is changing our lives and transforming every sector of the economy.

The deep learning revolution has brought us driverless cars, the greatly improved Google Translate, fluent conversations with Siri and Alexa, and enormous profits from automated trading on the New York Stock Exchange. Deep learning networks can play poker better than professional poker players and defeat a world champion at Go. In this book, Terry Sejnowski explains how deep learning went from being an arcane academic field to a disruptive technology in the information economy.

Sejnowski played an important…


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