ИИ в 2023: Топ 8 книг для глубокого погружения в мир искусственного интеллекта

Machine Learning Yearning

Author: Andrew Ng

The next free eBook was created by one of the most popular personalities in the AI industry. 

Andrew Ng is an adjunct professor at Stanford University and pioneer in online education. He is a co-founded Coursera and deeplearning.ai. He was also a co-founded Google Brain, a former Vice President and Chief Scientist at Baidu. His online courses were attended by over 2.5 million students from all over the world.

Machine Learning Yearning is focused on structuring machine learning projects. It explains how to make machine learning algorithms work. And once you’ve read it, you’ll know how to identify and prioritize the most promising aspects of your AI projects, diagnose errors in your ML systems, and perform several other vital tasks. To download the resource, head over to the website and fill out a short form.

Ethical Artificial Intelligence

Author: Bill Hibbard 

While it’s always good to improve your AI skills, there’s value in reading about the technology’s ethical challenges, too. 

After all — the discussion on whether AI is ‘good or evil’ rages on, and this book deals with the topic well. The author first presents the technical challenges of designing ethical AI, then makes a case for the various strategies for solving these issues. 

Overall, the book is easy to understand. The mathematical explanations are there for those who want that level of detail. But you can skip them and still follow the general arguments, all thanks to well-written text and thoughtful diagrams.

Advanced AI Books

If Inspired is about how to define the best product as a team, this is about how to deliver it. It’s really the DevOps equivalent of product definition. Once you get to the right product, how do you then continually deliver it? And that’s especially critical for AI, because you have more change streaming in from both data and the algorithm.

Software development has gone from annual releases to continuous deployment. Not everybody’s there, but most people are somewhere on the spectrum. With AI, we have to accelerate. Because not only are algorithms changing, but they then impact the software and technology around them. And you have the data that impacts the AI. Data models are constantly changing because the data is constantly changing. You’re dealing with a much more complex ecosystem, so we really need to adopt those principles. It’s really DevOps on steroids, right? Or chaotic DevOps.

That’s why this book is especially important for AI. If Inspired is the foundation, then Accelerate is what you really need to deliver AI. They complement each other — and they’re critical for AI because AI is more nebulous. We have to get these definitions down and we have to get delivery down. (Eggers)

You need to understand bias and the problems we can create with these algorithms. There are several good titles on this now, Weapons of Math Destruction and Algorithms of Oppression. I do think that Sara provides many different types of examples that are particularly related to technology and what’s happening with the digital transformation, which is where a lot of AI is coming in.

With some bias, the problem is the data has the bias built in. Even if you’re not putting the explicit tags of bias — gender, race, things like that — there’s so much built in and been reinforced because of what the human bias thinks already.

AI will pick up on our generalizations. That’s where we need to be careful about what data we give it to learn on. How do we make sure that we’re cognizant of what’s baked in, even when it’s not explicit? (Eggers)

RelatedArtificial Intelligence Careers: How to Break Into the AI Field, According to Experts

Interpretable Machine Learning by Christoph Molar

This is also very technical, very much a textbook, but it talks about some areas that are quite a bit more directly important to our clients . It’s a guide for making black boxes explainable.

Probably the defining problem of our day is that, as you start to become more sophisticated and your models become more complex, the ability to understand those models — why they’re doing what they’re doing, why they’re making the predictions that they’re making — becomes much more difficult.

That’s part of the double-edged sword in AI. AI has a lot of promise, but as you start to move toward that promise, your risks go up proportionately — where models do things that are not just mysterious, but potentially quite dangerous depending on the application. 

As AI develops, topics of interpretability and transparency are going to come up. And it’s going to provide a very serious check to the advancement of AI. Our point of view is that the only way to really keep up with this is to use more math, more data science. 

It’s like an arms race. As the math becomes more complex in making predictions, the math needed to interpret and understand those models as humans becomes more important and more advanced. So characteristics that allow us to understand why they’re saying what they say. And this book is one of very few that really covers that. There are a lot of papers on the topic but very few books. (Smith)

How Smart Machines Think

By Sean Gerrish

The algorithms and key ideas have been highlighted by which these intelligent machines perceive and interact with society and the world. This book thoroughly describes the software architecture of AI.  It explains how self-driven cars stay on the road and the passenger reaches the destination without any harm or hurdle, different algorithms by which TV shows and movies are recommended on different social websites, as well as how programmers are treating and programming the systems.

This book contains less technical details and more easy-to-understand text due to which science and technology geeks will find this book very pleasing and helpful because it’s a guide to the future in which the world will be dependent on these machines not on humans.

Artificial Intelligence: 101 Things You Must Know Today About Our Future

By Lasse Rouhiainen

This book is more focused on the importance of AI in the life of humans and its impacts that are both negative and positive. This book also has a post-COVID-19 section on AI. This book helps you learn the emerging technologies and their impacts on business, society, and humanity. All the hot topics of AI are well covered in this book from chatbots to robotic healthcare, smart cars to post covid-19 era, and much more.

This book also helps individuals learn to best prepare for the inevitable wave of science innovations with the help of practical and strategic tools. It also depicts the future of this technology; like robots revolutionizing industry and society, ethical standards and re-education are important for this generation and many more.

MLOPs

Deep learning in production by Sergios Karagianakos

Deep learning in production takes a hands-on approach to learn MLOps by doing. The premise of the book is that the reader starts with a vanilla deep learning model and works their way towards building a scalable web application. Full with code snippets and visualizations, it’s a great resource for ml researchers and data scientists with a limited software background.

Each chapter deals with a different phase of the machine learning lifecycle. After discussing the design phase, the reader will familiarize themselves with best practices on how to write maintainable deep learning code such as OOP, unit testing, and debugging. Chapter 5 is all about building efficient data pipelines, while Chapter 6 deals with model training in the cloud as well as various distributed training techniques.

Moving on, the book deals with serving and deployment techniques, while emphasizing on tools such as Flask, uWSGI, Nginx, and Docker. The final two chapters explore MLOPs. More specifically, they discuss how to scale a deep learning application with Kubernetes, how to build end-to-end pipelines with Tensorflow Extended, and how to utilize Google cloud and Vertex AI.

Some things to note:

  • The entire code is written with Tensorflow 2.0.

  • The book is quite opinionated in terms of libraries but tries to focus on the actual practices than the libraries themselves.

  • Sometimes it can feel a bit shallow because going into every last area is impossible. The goal is to guide the reader to understand the things they need to learn, not diving into every little detail.

Available in: Amazon, Leanpub, Goodreads, Website

Machine learning engineering by Andriy Burkov

Machine learning engineering is the second book by Burkov and is a great reference book of the entire ML lifecycle. Burkov does an excellent job aggregating design patterns and best practices on how to build machine learning applications. When I first read this book, I felt like it contained all of the google searches and browser bookmarks of my previous years.

Similar to the previous book, each chapter focuses on a separate phase of the ML lifecycle. Starting from the design phase, it describes the challenges and priorities of an ML project. Moving on to data processing and feature engineering, you will find clear explanations of frequently used industry terms, as well as common pitfalls with their corresponding solutions.

The training and evaluation phase is split into three chapters, where Burkov analyzes how to improve the accuracy of the model using techniques such as regularisation, hyperparameter tuning, and more. It also deals with problems such as distribution shift, model calibration, a/b testing. The final two chapters are my personal favorites, as they discuss deployment strategies, model serving, and maintenance.

Remarks:

  • The book focuses on the actual practices without providing many code examples and real-life applications.

  • is the main library used throughout the book. Different frameworks and tools are also mentioned but without going into many details.

  • Sometimes it can feel like a huge checklist of “good-to-know” concepts that someone could use for more research.

Available in: Read-first version, Amazon, Leanpub

Dive into Deep Learning

Authors: Aston Zhang, Zack C. Lipton, Mu Li, Alex J. Smola

The first eBook on our must-read list is a deep-dive into . The authors are Amazon employees who use Amazon’s MXNet library to teach Deep Learning. Importantly, they update their work regularly, so you can be sure you’re reading the latest, most current information. Recently, the authors have added new implementations to the book in two currently most popular DL libraries: Pytorch and Tensorflow/Keras, which is a significant advantage of this resource. 

The authors update their work regularly, so you can be sure you’re reading the latest, most current information. Another advantage of this book is its interactivity – readers can comment on each chapter and even ask and answer questions. What’s more, with just one click, you can turn the code from the book on the Google Colab GPU.

Machine Learning

Scala Machine Learning Projects (2018) Download

Machine learning has had a huge impact on academia and industry by turning data into actionable information. Scala has seen a steady rise in adoption over the past few years, especially in the fields of data science and analytics. This book is for data scientists, data engineers, and deep learning enthusiasts who have a background in complex numerical computing and want to know more hands-on machine learning application development.

Machine Learning with Swift (2018) Download

Machine learning as a field promises to bring increased intelligence to the software by helping us learn and analyse information efficiently and discover certain patterns that humans cannot. This book will be your guide as you embark on an exciting journey in machine learning using the popular Swift language.

Reinforcement Learning

Hands-On Reinforcement Learning with Python (2018) Download

Reinforcement Learning (RL) is the trending and most promising branch of artificial intelligence. Hands-On Reinforcement learning with Python will help you master not only the basic reinforcement learning algorithms but also the advanced deep reinforcement learning algorithms.

Deep Reinforcement Learning Hands-On (2018) Download

Recent developments in reinforcement learning (RL), combined with deep learning (DL), have seen unprecedented progress made towards training agents to solve complex problems in a human-like way. Google’s use of algorithms to play and defeat the well-known Atari arcade games has propelled the field to prominence, and researchers are generating new ideas at a rapid pace.

Neural Network

Neural Network Programming with Tensorflow (2017) Download

If you’re aware of the buzz surrounding the terms such as “machine learning,” “artificial intelligence,” or “deep learning,” you might know what neural networks are. Ever wondered how they help in solving complex computational problem efficiently, or how to train efficient neural networks? This book will teach you just that.

Practical Convolutional Neural Networks (2018) Download

Convolutional Neural Network (CNN) is revolutionizing several application domains such as visual recognition systems, self-driving cars, medical discoveries, innovative eCommerce and more.You will learn to create innovative solutions around image and video analytics to solve complex machine learning and computer vision related problems and implement real-life CNN models.

Approachable AI Books for Non-Technical Readers

AI for People and Business by Alex Castrounis

It’s becoming imperative for business leaders to understand artificial intelligence and machine learning at an appropriate level in order to build great data-centric products and solutions. Given that, I wrote AI for People and Businesses for executives, managers and non-technical folks that are interested in leveraging AI within their organization, and to fill a gap that I saw in the AI literature.

I also wrote it for practitioners interested in a business perspective around AI, to give them frameworks they can use to explain complex AI concepts to their company’s leadership. Because sometimes there’s a bit of a struggle there. At the end of the day, I think it will help people understand exactly what AI is and help them learn how to identify opportunities with AI. It’s really focused on developing and executing a successful AI vision and strategy as well.

And AI is hard to simplify because it’s inherently not simple. If you want, you can dive all the way down into vector calculus and matrix and linear algebra and statistics  — the list goes on. But it’s all about what level of granularity is right for what target audience. This book really simplifies all those very complex things in ways that benefit executives and managers. (Castrounis)

Related68 Artificial Intelligence (AI) Companies to Know

This kind of tells you, Okay, here’s where we are. We’re in a nascent state and we need to understand what that entails — where it’s strong and where it’s not.

The book makes where AI is more real. In my AI talks, I use a lot of examples that come from Amazon, looking at the recommendations you sometimes get for products and the challenges with that. I’m not picking on Amazon; I chose it because it’s something people can relate to.

And that’s what Jenelle does; she makes AI relatable. So people understand better where the technology is and some of the challenges that we might be coming across. Because people imagine AI as this beautiful, wonderful magic black box that’s smarter than them — and it’s not. Jenelle helps ground that for readers, so that they’re less scared of it and hopefully engage more with it. It’s a fun, easy read. (Eggers)

Inspired: How to Create Tech Products Customers Love by Marty Cagan

It’s not specifically about AI, but rather about how to deliver technology. It’s a great book for everyone from engineers to executives to management. I’ve given this book to all of them. Engineers have been like, “I never understood why it was so hard to work on my teams, and I’ve been part of the problem!” Or, “I’ve hated our designer all this time, and now I understand them and what their role is and what my role is!”

Cagan puts together a good framework for how to define and deliver products. The focus is technical products, but it’s good for products in general. You can read it quickly. If you have a three-hour flight, you can skim it and still pick up a lot. Marty’s very smart and has been in the industry for a long time.

And it was a personal journey for him: He started out as an engineer himself and was on a product that wasn’t successful. And he thought, But I delivered exactly on the MRD, or marketing requirements doc, so why did it fail? It’s either them or it’s me. Who was it? It’s a combination. (Eggers)

How the Mind Works by Steven Pinker

It’s not an AI book but it does have a section on building artificial intelligence.It’s just a preposterously good book just in general. It’s almost on par with The Selfish Gene type of overarching, broad view of the evolutionary effect on the human brain. How the Mind Works is a very nice high-level view over human brain function, not necessarily directly applicable to artificial intelligence, but it is a very, very good book. And Pinker does talk about AI there. (Smith)

Artificial Intelligence for Kids (Tinker Toddlers)

By Dr. Dhhot

This book has been focused on kids to equip and sharpen them to better understand the use of AI technology. Dr.Dhoot introduced the basic concepts of AI; its types and their meanings. Different approaches have been used to better deliver the idea of AI. Flamboyant, beautiful, and visually pulsating illustrations with simple text have been deployed to encourage the children’s sense of curiosity and wonder.

There are two levels; one is for basics for kids which is written in black text and the other level is in purple text for reading, learning, and beyond. It allows the kids to grasp scientific concepts in a fun and creative way.

Introducing Artificial Intelligence: A Graphic Guide

By Henry Brighton

This book focuses on human intelligence and also on artificial intelligence. Over the past 50 years, much research has been done on artificial intelligence and machines. The results of the research have a similar conclusion that computer intelligence beats human intelligence. This book also addresses major issues in the designing of machines, consciousness, tuning structural cutting of robots, and many more. This book broadly covers all fascinating areas of AI.

Stay tuned to AiHints for more insightful tutorials on web development, programming, and artificial intelligence. Happy coding!

Artificial Intelligence Basics: A Non-Technical Introduction

By Tom Taulli

This book summarizes the uses of AI in every sector of life. AI technology is present everywhere from digital assistants to smartphones. Artificial intelligence has become popular and it is also evolving as a general technology reflected in transportation, financial services, gaming industry, healthcare, and many more. Everyone needs to understand this technology for a better future and success.

The non-technical introduction has been given to many important concepts for readers such as deep learning, machine learning, natural processing language, and more. This book helps readers to grasp the fundamentals of AI. Attention has also been paid to the future importance of AI. Its impact on world governments, daily life tasks, industries, and companies’ structures has been highlighted. AI is the present and future of every business as well. Many tech giants and other industries are already making investments in AI to better equip themselves.

Reinforcement Learning

Authors: Richard S. Sutton, Andrew G. Barto

Reinforcement learning is one of AI’s most active research areas, and this book delivers a clear, straightforward interpretation of the field’s key ideas and algorithms. 

The authors divided their work into three parts, covering reinforcement learning without going beyond the tabular case for which exact solutions can be found — while you’ll learn how reinforcement learning relates to psychology and neuroscience. 

The last chapter also covers the future societal impacts of reinforcement learning. Better still, the book is full of recent case studies.

Artificial Intelligence: A Guide to Intelligent Systems

By Michael Negnevitsky

This book’s target audience is students. It helps the students utilize their thinking and understanding of AI to build intelligent systems that are based on knowledge. This will allow students to build systems like evolutionary computation, intelligent agent-based systems, neural networks, etc. There are no complex mathematical terms used in the book, so readers know exactly which technology is useful and which is not. This book describes the usage of tools with examples. It is good to minimize the programming complexity at the student level for AI and students can understand and benefit by discovering new tools and their usage.     

Life 3.0: Being Human in the Age of Artificial Intelligence

By Tegmark Max

This book shows the way to keep human interaction alive in different tasks that involve AI. In this way, prosperity can grow side by side with automation without leaving people lacking income or purpose. This book empowers readers to understand different aspects of artificial intelligence. It covers all the viewpoints whether they are controversial or not, from consciousness to superintelligence. It explains how AI can change the future and how can we make our future with the help of this intelligent technology. It highlights how this technology can affect crimes, law, war, jobs, and the sense of a human being. It shows the way by which human beings and AI can work together and avoid challenging factors.

Natural Language Processing

Hands-On Natural Language Processing with Python (2018) Download

Natural language processing (NLP) has found its application in various domains, such as web search, advertisements, and customer services, and with the help of deep learning, we can enhance its performances in these areas. Hands-On Natural Language Processing with Python teaches you how to leverage deep learning models for performing various NLP tasks, along with best practices in dealing with today’s NLP challenges.

Natural Language Processing with TensorFlow (2018) Download

Natural language processing (NLP) supplies the majority of data available to deep learning applications, while TensorFlow is the most important deep learning framework currently available. Natural Language Processing with TensorFlow brings TensorFlow and NLP together to give you invaluable tools to work with the immense volume of unstructured data in today’s data streams, and apply these tools to specific NLP tasks.

The Elements of Statistical Learning: Data Mining, Inference, and Prediction

Authors: Trevor Hastie, Robert Tibshirani, Jerome Friedman

The science of learning plays a crucial role in statistics, data mining, AI, and other disciplines. And while this book’s approach is statistical, the emphasis is on concepts in place of pure mathematics (which makes it a valuable resource for statisticians and anyone interested in data mining, in general). 

The authors focus on supervised learning (prediction) and unsupervised learning, covering topics like neural networks, classification trees, support vector machines, and boosting the first comprehensive treatment.

We hope you find this list helpful. If you know of any other valuable — and free — eBooks that we’ve missed off, please let us know: we’d be delighted to add as many useful resources as possible.

AI Books Focused on Deep Learning

This book provides a wider framework than just deep learning, which is the hot thing now. Two things to bear in mind: People should know about the different tribes, as the author calls them, and they should also understand that most solutions are going to be ensemble systems, meaning it’s not going to be one-tribe-takes-all. It’s going to be a combination of several. 

You see that even with what DeepMind did with AlphaGo, which used two tribes, arguably even three. So it’s a good framework, and it’s accessible. For technical people, it’s probably going to open their eyes to some things they didn’t know about, especially if they just got into AI in the latest craze. And it’s also accessible to business people, meaning it’s not too technical that they feel like they have to slog through it. It is a little more dry than my next pick, but will give you a spoonful of sugar to go with the shredded wheat — and I like shredded wheat, to be clear.

The author’s correct in that there are tribes and the tribes don’t often mix, but I think we need to encourage the tribes to mix more. I challenge with the whole “master algorithm” because there’s not going to be one. Like I said, it’s going to be an ensemble. Getting that across, and how to mix and match them . But I do think it’s a great initial framework. (Eggers)

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Rebooting AI: Building Artificial Intelligence We Can Trust by Gary Marcus and Ernest Davis

I see this book as being kind of a shot across the bow of the deep learning/connectionist camp, which has sort of taken over the discussion around artificial intelligence … There are several different traditional ML camps; connectionism is neural networks — same idea.

Rebooting AI argues, let’s take stock of artificial intelligence, our goals and what useful AI would look like, and ask ourselves, How close to this does deep learning … really get us? The thesis basically is: It gets us down the road in some ways, but in a whole host of areas it doesn’t get us anywhere we need to get. 

And all the attention applied to deep learning right now is, in the authors’ view, somewhat distracting from other areas that could yield fruit. They’re trying to encourage a broader view of AI, revisiting some of the more classical AI camps and disciplines — looking at work that’s 40 and 50 years old in some cases as being integral to the advancement of artificial intelligence. It’s a very good book that helps temper the euphoria over deep learning. (Smith)

Deep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville

This is one of the best textbooks I’ve ever read, period. It focuses on deep learning, but it covers the fundamentals of machine learning. It just does a very good job of being very information-dense but also very accessible. 

It’s very technical, so it’s probably not for everybody. It’s definitely not in the category of popular topics in AI. It’s an advanced textbook that would be taught in a graduate-level course, and would need a number of mathematics prerequisites to understand it. You can read it and get , but to actually treat it as a textbook, you’re in full-on grad-program mode at that point. (Smith) 

Neural Networks and Deep Learning by Michael Nielson

A free online book that’s very easy to read and understand, specifically about neural networks and deep learning. It includes a lot of helpful images, visualization and even some videos. And I really like the author’s writing style and voice.

I do a lot of speaking engagements and training workshops and I often get the question: I’m trying to get into AI or machine learning; what do you recommend for me? And unfortunately there’s not really a one-size-fits-all answer because it really depends on people’s learning styles. Some people like videos, some people like podcasts and some people learn better hands-on or reading a book. But I’m a book guy — the one-stop-shop organization and depth of focus. It’s hard to piece together that much knowledge and information, just scouring the web for like articles.

And I really like how Nielson (pictured upper left) writes. I even pinged him once, when I first read it a long time ago, just to tell him that I love the casual writing style. (Castrounis)

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Artificial Intelligence: A Modern Approach

By Stuart Russell and Peter Norvig

This book offers an up-to-date and comprehensive introduction to the theory and practice of AI. It offers a coherent view of artificial intelligence. AI algorithms are presented most simply. This book provides a telepath for readers to understand AI technology by first viewing the history and the past of this field and then the future of this technology and how we have come to this day with the integration of AI in daily life tasks.

This book conveys how we can use automation as much as we want without depending on it. The approach used by the author is very unique in a way that a reader can understand the full depth of basics and advances of AI without any complexity.

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