It’s been fantastic to work in computer vision, especially when it is used to build biometric systems. I and my 80 odd PhD students have pioneered systems that recognise people by the way they walk, by their ears, and many other new things too. To build the systems, we needed computer vision techniques and architectures, both of which work with complex real-world imagery. That’s what computer vision gives you: a capability to ‘see’ using a computer. I think we can still go a lot further: to give blind people sight, to enable better invasive surgery, to autonomise more of our industrial society, and to give us capabilities we never knew we’d have.
David Marr shaped the field of computer vision in its early days. His seminal book laid the structure for interpreting images and one which is still largely followed. He popularised notions of the primal sketch and his work on edge detection led to one of the most sophisticated approaches. His work and influence continue to endure despite his early death: we missed and miss him a lot.
Available again, an influential book that offers a framework for understanding visual perception and considers fundamental questions about the brain and its functions.
David Marr's posthumously published Vision (1982) influenced a generation of brain and cognitive scientists, inspiring many to enter the field. In Vision, Marr describes a general framework for understanding visual perception and touches on broader questions about how the brain and its functions can be studied and understood. Researchers from a range of brain and cognitive sciences have long valued Marr's creativity, intellectual power, and ability to integrate insights and data from neuroscience, psychology, and computation. This…
A noted quantitative hedge fund manager and quant finance author, Ernie is the founder of QTS Capital Management and Predictnow.ai. Previously he has applied his expertise in machine learning at IBM T.J. Watson Research Center’s Human Language Technologies group, at Morgan Stanley’s Data Mining and Artificial Intelligence Group, and at Credit Suisse’s Horizon Trading Group. Ernie was quoted by Bloomberg, the Wall Street Journal, New York Times, Forbes, and the CIO magazine, and interviewed on CNBC’s Closing Bell program. He is an adjunct faculty at Northwestern University’s Master’s in Data Science program and supervises student theses there. Ernie holds a Ph.D. in theoretical physics from Cornell University.
By now, you may notice that I like to recommend textbooks. I use this bestseller for my course in Financial Machine Learning at Northwestern University, but really, nobody interested in financial machine learning hasn’t read this book. The topics are highly relevant to every investor or trader – I read it at least 5 times to digest every nugget and have put them to very productive use in my trading as well as in my fintech firm predictnow.ai. It covers basic techniques such as random forest to advanced techniques such as Hierarchical Risk Parity, which is a big improvement over traditional portfolio optimization methods.
Marcos used to be Head of Machine Learning at AQR (AUM=$143B), and now is the Global Head of Quant Research at Abu Dhabi Investment Authority. He is also very approachable to his readers and students. There was seldom an email or message from me to which…
Learn to understand and implement the latest machine learning innovations to improve your investment performance
Machine learning (ML) is changing virtually every aspect of our lives. Today, ML algorithms accomplish tasks that - until recently - only expert humans could perform. And finance is ripe for disruptive innovations that will transform how the following generations understand money and invest.
In the book, readers will learn how to:
Structure big data in a way that is amenable to ML algorithms
Conduct research with ML algorithms on big data
Use supercomputing methods and back test their discoveries while avoiding false positives
I am motivated by working on products that many people use. I've been a part of companies that deliver products impacting millions of people. To achieve it, I am working in the Big Data ecosystem and striving to simplify it by contributing to Dremio's Data LakeHouse solution. I worked on projects using Spark, HDFS, Cassandra, and Kafka technologies. I have been working in the software engineering industry for ten years now, and I've tried to share my experience and lessons learned in the Software Mistakes and Tradeoffs book, hoping that it will allow current and the next generation of engineers to create better software, leading to more happy users.
Every enterprise application creates data, whether it's log messages, metrics, user activity, outgoing messages, or something else. And how to move all of this data becomes nearly as important as the data itself. If you're an application architect, developer, or production engineer new to Apache Kafka, this practical guide shows you how to use this open source streaming platform to handle real-time data feeds.
Engineers from Confluent and LinkedIn who are responsible for developing Kafka explain how to deploy production Kafka clusters, write reliable event-driven microservices, and build scalable stream-processing applications with this platform. Through detailed examples, you'll learn Kafka's…
A great way to see how AI is being used by companies and not just the future predictions of how AI could be used. Made me more open to how AI will change my industry (retail) and how people can use it to make better decisions. It kicked off my current journy to become more AI aware. As always, I appreciate anything that Thomas Davenport writes.
A fascinating look at the trailblazing companies using artificial intelligence to create new competitive advantage, from the author of the business classic, Competing on Analytics, and the head of Deloitte's US AI practice.
Though most organizations are placing modest bets on artificial intelligence, there is a world-class group of companies that are going all-in on the technology and radically transforming their products, processes, strategies, customer relationships, and cultures.
Though these organizations represent less than 1 percent of large companies, they are all high performers in their industries. They have better business…
Like
professionals in other industries, educators are recognizing the power of data
and are using it to guide their decision-making. Yet quantifying what works and
what doesn’t when it comes to something as variable-rich as learning is
extremely difficult.
Hence, as bright as they are, educators have only a 14%
accuracy rate when interpreting student data. Fortunately, authors Rees and
Wynns have exactly what is needed to remedy this problem.
They offer the
hard-but-important-to-look-at facts concerning data use in our schools and pair
it with a clear path to fixing problems. They pull engaging stories from their
extensive experience and have a whip-smart writing style I envy.
One wonders
how a book on data that uncovers harsh realities can be such an enjoyable read –
the kind too enthralling to put down.
This book helps school and district leaders avoid the pitfalls that await those making sense of their school's data. Whether you're interpreting achievement gaps, graduation rates or test results, you're at risk of reaching a mistaken judgment. By learning about common errors and how they're made, you'll be ready to choose safer, surer paths to making better sense of the wealth of data in your school or district. The authors help educators build better evidence, see conclusions more clearly, and explain the data more persuasively.
Special features Include:
"Questions to Spark Discussion" in each chapter encourage school site, district leaders,…
I have read most of the books written about Apollo, especially those ostensibly written by my fellow participants. I have read these books for pleasure, to find out about parts of the moon effort that I did not see first-hand, and to learn what I could from the authors’ mistakes and successes — with a view to the writing of my own book. The books I have come to value the most are the books that seem to have been created for some other reason than commercial gain, the books unmarred by ghostwriting or heavy-handed editing, the books where the author’s authentic voice speaks from the page.
Eldon Hall led the development of the Apollo Guidance Computer, that one-cubic-foot device with 76kb of memory that navigated, guided, and controlled each of the Apollo spacecraft — the machine that I helped program. His book is both a detailed description of the Apollo computer and a history of its development. The most dramatic chapter chronicles the bold decision to use integrated circuits in the design of the computer — all of the same type, to encourage the vendor to keep making them — although that technology was then anything but reliable.
The first of its kind, Journey to the Moon details the history and design of the computer that enabled U.S. astronauts to land on the moon. The book recalls the history of computer technology, both hardware and software, and the applications of digital computing to missile guidance systems and manned spacecraft. The book also offers graphics and photos drawn from the Draper Laboratories' archives that illustrate the technology and related events during the Apollo project. Written for experts as well as lay persons, Journey to the Moon is the first book of its kind and a must for anyone interested…
I’ve been making web pages since the World Wide Web began in the mid-1990s. Back then, the web was visually quite sparse. It wasn’t until the late 2000s that new browser capabilities let the web getvisually interestingand an exciting place for interactive graphics. Graphics are great: they can be informational (like charts and maps) or purely aesthetic. My personal journey of learning to code interactive graphics has been so rewarding that I’ve shared the love with others through teaching creative coding workshops and undergraduate courses. If you’re new to coding or computer graphics, I hope you’ll give one of these books a try!
If I were getting started with coding graphics today, I would start with this book, hands down. Learning p5 is the easiest way to create interactive graphics that run in a web browser, and this book is a very friendly, accessible, and beautifully illustrated introduction to coding graphics with p5.js—no prior experience needed. You might be wondering about the name “p5.js”. It’s a JavaScript library (that’s the “.js” part) based on Processing, the open-source programming language created for artists and designers. (More on Processing in a moment.) I have taught college courses with this book, and students love it. Plus, all the skills you learn here with p5 are applicable to JavaScript—the world’s most popular programming language—more generally.
Processing opened up the world of programming to artists, designers, educators, and beginners. The p5.js JavaScript implementation of Processing reinterprets it for today's web. This short book gently introduces the core concepts of computer programming and working with Processing. Written by the co-founders of the Processing project, Reas and Fry, along with Lauren McCarthy, one of the minds behind p5.js, Getting Started with Processing gets you in on the fun!
I love computers, and especially computer systems. I’m interested in how different pieces of hardware and software, like processors, operating systems, compilers, and linkers, work together to get things done. Early in my career, as a software security tester, I studied how different components interacted to find vulnerabilities. Now that I work on compilers, I focus on the systems that transform source code into a running program. I’m also interested in how computer systems are shaped by the people who build and use them—I believe that creating safer, more reliable software is a social problem as much as a technical one.
Although I don’t work in government, this is a book I’ll come back to whenever I need a reminder to put user needs ahead of process or wisdom about how to work inside a large bureaucracy to make that happen. Where Meltdown focuses on spectacular blow-ups, this book explores run-of-the-mill failures—like long, complicated online forms and websites that only load in specific, outdated browsers. (Though bigger failures, like the launch of healthcare.gov, get airtime too.)
I appreciated this book’s thoughtful analysis of how government software gets built—it goes beyond the stereotype of the incompetent government employee and digs into the underlying reasons that even competent and dedicated public servants can struggle to deliver critical software. Many of those reasons apply to private companies, too.
Learn more about Jennifer Pahlka's work at recodingamerica.us.
"The book I wish every policymaker would read." -Ezra Klein, The New York Times
A bold call to reexamine how our government operates-and sometimes fails to-from President Obama's former deputy chief technology officer and the founder of Code for America
Just when we most need our government to work-to decarbonize our infrastructure and economy, to help the vulnerable through a pandemic, to defend ourselves against global threats-it is faltering. Government at all levels has limped into the digital age, offering online services that can feel even more cumbersome than the paperwork that…
I was trained as a mathematician but have always been motivated by problem-solving challenges. Statistics and analytics combine mathematical models with statistical thinking. My career has always focused on this combination and, as a statistician, you can apply it in a wide range of domains. The advent of big data and machine learning algorithms has opened up new opportunities for applied statisticians. This perspective complements computer science views on how to address data science. The Real Work of Data Science, covers 18 areas (18 chapters) that need to be pushed forward in order to turning data into information, better decisions, and stronger organizations
The text covers classic statistical inference, early computer-age methods, and twenty-century topics. This puts a unique perspective on current analytic technologies labeled machine learning, artificial intelligence, and statical learning. The examples used provide a powerful description of the methods covered and the compare and contrast sections highlight the evolution of analytics. This book by Efron and Hastie is a natural follow-up source for readers interested in more details.
The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and influence. 'Data science' and 'machine learning' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? How does it all fit together? Now in paperback and fortified with exercises, this book delivers a concentrated course in modern statistical thinking. Beginning with classical inferential theories - Bayesian, frequentist, Fisherian - individual chapters take up a series of influential topics: survival analysis, logistic…
I’m an Associate Professor in the University of Alabama’s Department of Philosophy. I worked as an FBI Special Agent before making the natural transition to academic philosophy. Being a professor was always a close second to Quantico, but that scene in Point Break in which Keanu Reeves and Patrick Swayze fight Anthony Kiedis on the beach made it seem like the FBI would be more fun than academia. In my current position as a professor at the University of Alabama, I teach in my department’s Jurisprudence Specialization. My primary research interests are at the intersection of philosophy of law, political philosophy, and criminal justice. I’ve written three books on policing.
I love this book because it reminds us of the many ways that technology can affect justice.
It is tempting to think sophisticated tactics such as “predictive policing” can solve all problems relating to human bias. However, Brayne shows that data and algorithms do not eliminate bias and discretion. Instead, high-tech police tools simply make bias less overt and visible, which erodes the public’s ability to hold the police accountable.
I especially enjoyed how the book flips the script, considering diverse ways to use these tools to help the public. For example, how can municipalities use technology to analyze the underlying factors that contribute to policing problems in the first place?
The scope of criminal justice surveillance, from the police to the prisons, has expanded rapidly in recent decades. At the same time, the use of big data has spread across a range of fields, including finance, politics, health, and marketing. While law enforcement's use of big data is hotly contested, very little is known about how the police actually use it in daily operations and with what consequences.
In Predict and Surveil, Sarah Brayne offers an unprecedented, inside look at how police use big data and new surveillance technologies, leveraging on-the-ground fieldwork with one of the most technologically advanced law…
