I have been building real-time, production machine learning models for over 20 years. My book, and my book recommendations, are informed by that experience. I have a lot of empathy for people who are new to machine learning because I’ve taught courses on the topic. I founded the Advanced Solutions Lab at Google where we helped data scientists working for Google Cloud customers (who already knew ML) become ML engineers capable of building reliable ML models. The first two are the books I’d recommend today to newcomers and the last three to folks attending the ASL.
This recommendation is a bit of a cheat — I’m not recommending this exact book, but one of the books in the series that this book is part of.
Once you have the first two books under your belt, you’ll know how to solve ML problems. But you will keep reinventing the wheel. What you need next is a book on common “ML tricks” — best practices and common techniques when doing ML in production.
The problem is that these tricks are specific to the type of data that you will be processing. If you are going to be processing images or time series, read the corresponding books in the same series instead.
Many books and courses tackle natural language processing (NLP) problems with toy use cases and well-defined datasets. But if you want to build, iterate, and scale NLP systems in a business setting and tailor them for particular industry verticals, this is your guide. Software engineers and data scientists will learn how to navigate the maze of options available at each step of the journey.
Through the course of the book, authors Sowmya Vajjala, Bodhisattwa Majumder, Anuj Gupta, and Harshit Surana will guide you through the process of building real-world NLP solutions embedded in larger product setups. You'll learn how to…
I’ve spent most of my life writing code—and too much of that life teaching new programmers how to write code like a professional. If it’s true that you only truly understand something after teaching it to someone else, then at this point I must really understand programming! Unfortunately, that understanding has not led to an endless stream of bug-free code, but it has led to some informed opinions on programming and books about programming.
Yes, it’s a textbook, albeit a particularly well-written one. You may already have it on your shelf, if you’ve taken a programming class or two.
I’m way too old to have used CLRS as a textbook, though! For me, it’s an effectively bottomless collection of neat little ideas—an easy-to-describe problem, then a series of increasingly clever ways to solve that problem. How often do I end up using one of those algorithms? Not very often! But every time I read the description of an algorithm, I get a nugget of pure joy from the “aha” moment when I first understand how it works.
I am the co-author of Small Teaching K-8. I hold Massachusetts teacher licensure in English 5-12, Library k-12, and School Administration 5-8 as well as an M.Ed. from Boston College.
Why should we be emphasizing creativity in classrooms? In short order, our students’ careers will require them to augment the work of machines.
ChatGPT and DALI-2 are only the beginning. Du Sautoy explores the implications of artificial intelligence on the future of work. The Creativity Code is a reminder that technology is only as creative as its programmers—at least, for now.
Will a computer ever compose a symphony, write a prize-winning novel, or paint a masterpiece? And if so, would we be able to tell the difference?
As humans, we have an extraordinary ability to create works of art that elevate, expand and transform what it means to be alive.
Yet in many other areas, new developments in AI are shaking up the status quo, as we find out how many of the tasks humans engage in can be done equally well, if not better, by machines. But can machines be creative? Will they soon be able to learn from the…
As a child of the microcomputer revolution in the late 1970s, I’ve always been fascinated by the concept of a general-purpose machine that I could control. The deep learning revolution of 2010 or so, followed most recently by the advent of large language models like ChatGPT, has completely altered the landscape. It is now difficult to interpret the behavior of these systems in a way that doesn’t argue for intelligence of some kind. I’m passionate about AI because, decades after the initial heady claims made in the 1950s, AI has reached a point where the lofty promise is genuinely beginning to be kept. And we’re just getting started.
Goodfellow’s Deep Learning is a must in the field because it was the first. Prince’s new book is an essential follow-up to be up-to-date with the latest model types, including diffusion models (think Stable Diffusion or DALL-E), transformers (the heart of large language models), graph networks (reasoning over relationships), and reinforcement learning.
The math level is similar to what you’ll find in Goodfellow’s book.
An authoritative, accessible, and up-to-date treatment of deep learning that strikes a pragmatic middle ground between theory and practice.
Deep learning is a fast-moving field with sweeping relevance in today’s increasingly digital world. Understanding Deep Learning provides an authoritative, accessible, and up-to-date treatment of the subject, covering all the key topics along with recent advances and cutting-edge concepts. Many deep learning texts are crowded with technical details that obscure fundamentals, but Simon Prince ruthlessly curates only the most important ideas to provide a high density of critical information in an intuitive and digestible form. From machine learning basics to advanced…
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.
The advances of deep learning have been awesome, and fast. It’s been hard for the textbooks to keep up, so it’s good to include one that describes the advances and state of art very well. It seems appropriate that it’s edited by two leading researchers who are Roy – who described computer vision systems implementations in a long series of excellent books – and Matt, whose work on face recognition revolutionised and transformed the progress of face recognition in the 1990s. This book gives you an image of where we are now in computer vision, and where we are going.
Advanced Methods and Deep Learning in Computer Vision presents advanced computer vision methods, emphasizing machine and deep learning techniques that have emerged during the past 5-10 years. The book provides clear explanations of principles and algorithms supported with applications. Topics covered include machine learning, deep learning networks, generative adversarial networks, deep reinforcement learning, self-supervised learning, extraction of robust features, object detection, semantic segmentation, linguistic descriptions of images, visual search, visual tracking, 3D shape retrieval, image inpainting, novelty and anomaly detection.
This book provides easy learning for researchers and practitioners of advanced computer vision methods, but it is also suitable as…
I became fascinated by the highest achievements of human intelligence while a graduate student in philosophy working on the discovery and justification of scientific theories. Shortly after I got my PhD, I started working with cognitive psychologists who gave me an appreciation for empirical studies of intelligent thinking. Psychology led me to computational modeling of intelligence and I learned to build my own models. Much later a graduate student got me interested in questions about intelligence in non-human animals. After teaching a course on intelligence in machines, humans, and other animals, I decided to write a book that provides a systematic comparison: Bots and Beasts.
Stanislas Dehaene is one of the leading European cognitive scientists and this book provides a deep discussion of the neuroscience of learning, a key component of intelligence. He makes a strong case that current machine learning techniques are inferior to the processes that operate in human brains even in the womb. He draws out important implications for education concerning how people learn best.
"There are words that are so familiar they obscure rather than illuminate the thing they mean, and 'learning' is such a word. It seems so ordinary, everyone does it. Actually it's more of a black box, which Dehaene cracks open to reveal the awesome secrets within."--The New York Times Book Review
An illuminating dive into the latest science on our brain's remarkable learning abilities and the potential of the machines we program to imitate them
The human brain is an extraordinary learning machine. Its ability to reprogram itself is unparalleled, and it remains the best source of inspiration for recent…
This book is a collection of brilliant insights about how big tech has stolen our ability to concentrate.
I am very passionate about this topic since I have written a book about the subject and experienced being addicted to social media, YouTube, and other digital distractions and then managed to break free. I read the entire book in a few days, neglecting other things as I just couldn’t stop. It is a page-turner in the truest sense.
The writing is brilliant, and it was eye-opening to read about Hari’s experiment with a no-tech vacation and how his perspective and life experience changed because of it.
THE SUNDAY TIMES AND NEW YORK TIMES BESTSELLER
A SPECTATOR AND FINANCIAL TIMES BEST BOOK OF 2022
'If you read just one book about how the modern world is driving us crazy, read this one' TELEGRAPH
'This book is exactly what the world needs right now' OPRAH WINFREY
'A beautifully researched and argued exploration of the breakdown of humankind's ability to pay attention' STEPHEN FRY
'A really important book . . . Everyone should read it' PHILIPPA PERRY
Why have we lost our ability to focus? What are the causes? And, most importantly, how do we get it back?
For…
I am a financial data scientist. I think it is important that data scientists are highly specialized if they want to be effective in their careers. I run a business called Conlan Scientific out of Charlotte, NC where me and my team of financial data scientists tackle complicated machine learning problems for our clients. Quant trading is a gladiator’s arena of financial data science. Anyone can try it, but few succeed at it. I am sharing my top five list of math books that are essential to success in this field. I hope you enjoy.
This book might as well be called Introduction to machine learning, and it is probably one of the only books truly deserving of the title. Did you know neural networks have been used for decades to scan checks at the bank? They are called Boltzman Machine. Have you ever heard of how decision trees were used in old-school data mining? You could only get them from proprietary software packages from the early 2000s.
In quant trading, you will constantly face compute power constraints, so it is invaluable to understand the mathematical foundations of the most old-school machine learning methods out there. Researchers 20 years ago used to do a lot of impressive work with a lot less computing power.
This book describes the important ideas in a variety of fields such as medicine, biology, finance, and marketing in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of colour graphics. It is a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book.
I have been a machine learning engineer applying my ML expertise in computational advertising, and search domain. I am an author of 8 machine learning books. My first book was ranked the #1 bestseller in its category on Amazon in 2017 and 2018 and was translated into many languages. I am also a ML education enthusiast and used to teach ML courses in Toronto, Canada.
Another practical book that I highly recommend. Its intuitive structure is the first thing I like about it. It gives you a comprehensive walkthrough of the ML workflow, from data exploration to learning. It covers abundant practical guides that get you prepared for real world challenges, such as how to handle outliers and to impute missing data. As a ML practitioner, I appreciate the dedicated case studies throughout the entire book. They really excite learners for future real world applications.
The second edition of a comprehensive introduction to machine learning approaches used in predictive data analytics, covering both theory and practice.
Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application…
Saying just the right words in just the right way can cause a box of electronics to behave however you want it to behave… that’s an idea that has captivated me ever since I first played around with a computer at Radio Shack back in 1979. I’m always on the lookout for compelling ways to convey the topic to people who are open-minded, but maybe turned off by things that are overly technical. I teach computer science and study artificial intelligence as a way of expanding what we can get computers to do on our behalf.
So much of the public conversation around AI focuses on the extremes: "It's Going to Take Our Jobs And We'll Never Be Able To Work Ever Again!" or "It's Going To Create a Utopia And We'll Never Have To Work Ever Again!"
To be honest, I don't put a lot of credence into either of these perspectives. What I adore about this book is that it puts the technology in perspective in a concrete and laugh-out-loud funny way. Through detailed examples, it provides a glimpse into how the technology works, how it can be applied to real problems, and where it falls jaw-droppingly short.
“A deft, informative, and often screamingly funny primer on the ways that machine learning can (and often does) go wrong.” —Margaret Harris, Physics World
“You look like a thing and I love you” is one of the best pickup lines ever…according to an artificial intelligence trained by the scientist Janelle Shane, creator of the popular blog AI Weirdness. Shane creates silly AIs that learn how to name colors of paint, create the best recipes, and even flirt (badly) with humans—all to understand the technology that governs so much of our human lives.
