Saying just the right words in just the right way can cause a box of electronics to behave however you want it to behaveā¦ thatās an idea that has captivated me ever since I first played around with a computer at Radio Shack back in 1979. Iām 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.
āA deft, informative, and often screamingly funny primer on the ways that machine learning can (and often does) go wrong.ā āMargaret Harris, Physics World
āYou look like a thing and I love youā is one of the best pickup lines everā¦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 humansāall to understand the technology that governs so much of our human lives.
Iāve been a geeky kid all my life. (I donāt think Iāve quite grown up yet.) Born in the 1970s, my childhood was a wonderful playground of building robots and software. I was awarded one of the early degrees in AI, and a PhD in genetic algorithms. Iāve since spent 25 years exploring how to make computers think, build, invent, composeā¦ and Iāve also spent 20 years writing popular science books. Iām lucky enough to be a Professor in one of the worldās best universities for Computer Science and Machine Learning: UCL, and I guess Iāve written two or three hundred scientific papers over the years. I still think I know nothing at all about real or artificial intelligence, but then does anyone?
Iāve not met Harry, but he seems to have a logical and sensible head on his shoulders. His writing is considered and grounded, which is exactly what you need when discussing the hype that forever seems to surround AI. This book is another look at this topic and finds yet more ways to explain to readers the difference between human intelligence and our algorithmic attempts at intelligence ā which are frequently pretty stupid.
Recent startling successes in machine intelligence using a technique called 'deep learning' seem to blur the line between human and machine as never before. Are computers on the cusp of becoming so intelligent that they will render humans obsolete? Harry Collins argues we are getting ahead of ourselves, caught up in images of a fantastical future dreamt up in fictional portrayals. The greater present danger is that we lose sight of the very real limitations of artificial intelligence and readily enslave ourselves to stupid computers: the 'Surrender'.
By dissecting the intricacies of language use and meaning, Collins shows how farā¦
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 applicationā¦
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 Iāve 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 Iād recommend today to newcomers and the last three to folks attending the ASL.
There are three types of machine learning books ā books written for people who want to become machine learning engineers, books written for people who want to become machine learning researchers, and books written for business executives. Reading a book written for researchers or executives can be a frustrating experience if you are a software engineer, social scientist, or mechanical engineer who wants to learn machine learning and get an ML job in the industry.
If you are a coder who wants to become an ML engineer, you have got to learn machine learning concepts, but you want to learn them in a practical way. You need a book that leads with intuition and shows you implementations with code. It has to do this without getting sidetracked into ML theory, getting mired in statistical concepts, or being so superficial that you donāt understand why the code works.ā¦
Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how.
By using concrete examples, minimal theory, and two production-ready Python frameworks-Scikit-Learn and TensorFlow-author Aurelien Geron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You'll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to helpā¦
As a child of the microcomputer revolution in the late 1970s, Iāve 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 doesnāt argue for intelligence of some kind. Iām 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 weāre just getting started.
Goodfellowās Deep Learning is a must in the field because it was the first. Princeās 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 youāll find in Goodfellowās 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 todayās 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 advancedā¦
Iāve been teaching and writing Python code (and managing others while they write Python code) for over 20 years. After all that time Python is still my tool of choice, and many times Python is the key part of how I explore and think about problems. My experience as a teacher also has prompted me to dig in and look for the simplest way of understanding and explaining the elegant way that Python features fit together.
I like this book not just because itās a complete guide to the many ins and outs of data cleaning with Python, but also because David lays out the types of problems and the issues behind them. There are always trade-offs in data cleaning and this book lays out those trade-offs better than any other Iāve seen. This is one of the few books that as I go through it, I struggle to think of anything that could have been said better.
Think about your data intelligently and ask the right questions
Key Features
Master data cleaning techniques necessary to perform real-world data science and machine learning tasks
Spot common problems with dirty data and develop flexible solutions from first principles
Test and refine your newly acquired skills through detailed exercises at the end of each chapterBook Description
Data cleaning is the all-important first step to successful data science, data analysis, and machine learning. If you work with any kind of data, this book is your go-to resource, arming you with the insights and heuristics experienced data scientists had to learn theā¦
As a young businessperson in London in my early 30s, I was as ignorant of neurodiversity as much of the rest of the world. In the mid-2010s, I got fascinated by the topic thanks to conversations with autistic family members, who encouraged me to bring some of my expertise in corporate diversity programs to the field of āneurodiversity at workā. The topic of neurodiversity chimed with me, too, as Iļæ½ļæ½d suffered a traumatic brain injury in a serious car accident, and there were aspects I could relate to. I founded neurodiversity training company Uptimize to help ensure organizations across the world understand how the importance of embracing and leveraging different types of thinkers.
Explaining Humans engagingly begins, āIt was five years into my life on Earth that I started to think Iād landed in the wrong place. I must have missed the stop.ā
Part popular science, part memoir, part clarion call for neuroinclusion, Pangās book is full of sophisticated and memorable observations about humans, neurodiversity, and Pangās own neurodivergence.
I particularly enjoyed her comparison of the teamwork between human cells (neutral, effective, politics-free!) with that of typical human collaborationā¦and how much it made me realize that we can all substantially improve the latter at work to get the best out of each other and fulfill our collective potential.
WINNER OF THE ROYAL SOCIETY INSIGHT INVESTMENT SCIENCE BOOK PRIZE 2020
How proteins, machine learning and molecular chemistry can teach us about the complexities of human behaviour and the world around us
How do we understand the people around us? How do we recognise people's motivations, their behaviour, or even their facial expressions? And, when do we learn the social cues that dictate human behaviour?
Diagnosed with Autism Spectrum Disorder at the age of eight, Camilla Pang struggled to understand the world around her and the way people worked. Desperate for a solution, Camilla asked her mother if there wasā¦
I started my career in neuroscience. I wanted to understand brains. That is still proving difficult, and somewhere along the way, I realized my real motivation was to build things, and I wound up working in AI. I love the elegance of mathematical models of the world. Even the simplest machine learning model has complex implications, and exploring them is a joy.
Of course, this is not the obvious book to recommend for reinforcement learning, but if you are a beginner, then itās a quick and easy place to start. Itās compact and gets straight into the main algorithms.
It has a good balance between theory and code and will get you up and running quickly.
The Contemporary Introduction to Deep Reinforcement Learning that Combines Theory and Practice
Deep reinforcement learning (deep RL) combines deep learning and reinforcement learning, in which artificial agents learn to solve sequential decision-making problems. In the past decade deep RL has achieved remarkable results on a range of problems, from single and multiplayer games-such as Go, Atari games, and DotA 2-to robotics.
Foundations of Deep Reinforcement Learning is an introduction to deep RL that uniquely combines both theory and implementation. It starts with intuition, then carefully explains the theory of deep RL algorithms, discusses implementations in its companion software library SLMā¦
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 recentā¦
I was stimulated by Norbert Wienerās āCyberneticsā to study circuits in the brain that control behavior. For my graduate studies, I chose the olfactory bulb for its experimental advantages, which led to constructing the first computer models of brain neurons and microcircuits. Then I got interested in how the smell patterns are activated when we eat food, which led to a new field called Neurogastronomy, which is the neuroscience of the circuits that create the perception of food flavor. Finally, because all animals use their brains to find and eat food, the olfactory system has provided new insights into the evolution of the mammalian brain and the basic organization of the cerebral cortex.
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 fromā¦
How deep learningāfrom Google Translate to driverless cars to personal cognitive assistantsāis 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.
