The Ponicode 2020 Artificial Intelligence reading list
The books you need if you want to learn
Intro to machine Learning with Python
Author: Andreas Müller Sarah Guido
Is it time? Have you finally decided to get your head into Machine Learning? This book will be perfect for you to get started. You will need to be familiar with Python in order to get the best of it but you won’t need any previous machine learning knowledge. This book will lay out all the concepts of machine learning, including extended sections on supervised and unsupervised learning. Overall the book doesn’t get too much into mathematics but it will provide very good coding examples for you to get a solid understanding of machine learning. This book is very good at the beginning of your learning machine journey, you will quickly drift away from this to higher level resources once you have covered the basics. For those who want to get started with machine learning and don’t know where to start this is a very good option.
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
Author: Aurelien Geron
The prime choice of several software engineers in our team. They recommend this for the great amount of material it covers. A great mix of theoretical and practical explanations with detailed examples throughout. This book explains the basic concepts in a clear and accessible language. It also achieves to really dive under the hood and to provide complex examples. We describe it as a fantastic introduction to TensorFlow but the book really balances the Scikit section and the TensorFlow section so you’ll get a good taste of both.
Deep Learning by Ian Goofellow will feel like a natural next step read once you have finished this book.
Authors: Ian Goodfellow, Yoshua Bengio, Aaron Courville
This book is a solid beginner to intermediate transition for those who want to learn about machine learning. There is no coding in it. You will focus on neural networks and how to optimize them so be prepared for a mathematics-heavy read. We appreciate how rigorous is the content of “Deep learning”. You will have a better understanding of networks, regularization and algorithm optimization amongst many more concepts essential to deep learning. This book aims to become a bible that you would keep at your desk for regular consultation. It does not get very deep into each topic but it gives you a good 360 view on machine learning. The only downside to it would be that it lacks practical examples and exercises so you will need other resources along this book to progress.
Artificial Intelligence: A Modern Approach
Authors: Stuart Russel, Peter Norvig
Artificial Intelligence: A modern Approach is one of the best known books since many universities put it on their reading list. It was written by two reference authors in the field. You will find all the algorithms that are applied in artificial intelligence and it teaches you the math behind them. Unlike many other books it’s not just covering theory but you have a real practical approach to artificial intelligence. Be prepared for a heavy read as this is purely academic content and it is designed for the ones who want to move towards an advanced mastership of Machine Learning.
Pattern Recognition and Machine Learning
Author: Christopher M. Bishop
Your go to book to get started with pattern recognition. It was the school handbook of some data scientists of our team. You won’t need previous knowledge on pattern recognition to understand this book but you will need a solid math background. This intermediate level book is a great addition to your Machine Learning reading list and will get you a deeper understanding in the math behind Machine Learning (get ready for some Bayesian learning). Be prepared for this math-formula-heavy best seller.
The Master Algorithm
Author: Pedro Domingos
While not being a technical book per say The Master Algorithm is definitely addressing a non beginner audience. Pedro Domingos explores the different approaches to machine learning and with a refreshing tone slowly introduces his point of view on the future of AI. A good discussion maker and a great book to get if you want to step away from the technical aspects and think about the bigger picture. This book was published a few years ago but it still brings a very interesting angle to the AI discussion. It certainly is a good read that will entertain you more than the usual learning bibles that one has to go through.
The book for your IT team
The Hundred-Page Machine Learning Book
Author: Andriy Burkov
The promise of this book is to keep it short. And it delivers! Within the small amount of pages you will get the chance to have an understanding of the different techniques in Machine Learning with no more information than you need. Even if it’s a slim book you will get a fair share of theory and math. It’s direct, it’s essential, it’s for people who don’t have time for another Machine Learning book and yet want to have an understanding of the mechanisms of machine learning. A few companies recommend it to their software engineers. Even though Machine Learning advanced people won’t find anything new in this book it is recommended to software engineers that might interact from the distance with Machine Learning in their job and want to have a better understanding of it.
The books to be new-world-order-ready
AI Superpowers: China, Silicon Valley, and the New World Order
Author: Kai-Fu Lee
Further away from the usual learning book we recommend AI Superpowers, a great book about the space AI has in the current geopolitical landscape. You will find out how important AI will be in the new world order and specifically how it is at the heart of the US-China competition. While keeping busy learning about ML we tend to forget that deep tech and AI are the ultimate resources super powers are fighting for. GoT looks almost boring compared to the thrill this book will give you knowing that you are living in a defining era for humankind.
The book to share with your parents
Author: Hanna Fry
If you want to talk to your friends about Machine Learning and all they have is a vague idea of it that includes Will Smith in I,Robot then you should introduce them to Hannah Fry’s Hello World.
This book is meant to a broad audience. Hannah Fry is known for her ability to bring complex mathematics to the public. It makes a good summary on where machine learning is at, what applications and what future we can foresee for it. While not being a technical book it definitely elevates its audience to Machine Learning topics. It will bring an understanding of the real potential and limitations of ML and get your friends away from the basic “is AI dangerous for humankind” conversation starter.
If you want a taste of Hannah Fry’s humour and knowledge you can watch this brilliant talk she gave last year.
The book to get an ethic check
Weapons of Math Destruction
Author: Cathy O’Neil
Cathy O’neil explains in this book how big data, data mining and algorithms can have dangerous consequences using use cases in the recent years. She goes across a wide range of industries and lays out examples where data negatively impacted end users. A book that wakes us up to the danger of data manipulation when it is led by greed and profit. It unfortunately dresses a grim image of the use of algorithms but it acts as a wake up call towards our individual responsibility as business owners, developers and users. A good entry point to start thinking about the ethics of algorithms and how we all have a responsibility as watchmen to make sure the technology around us is not damaging to our community. Beware of the many american references that might go over your head if you are non american.
Life 3.0: Being Human in the Age of Artificial Intelligence
Author: Max Tegmark
Max Tegmark analyses from a societal point of view the potential consequences of Artificial Intelligence. A book that does not try to answer questions but rather to raise them. It will help you shape your own opinion on this major topic by exploring the possibilities of AI and consequently the limits of what AI should or shouldn’t be able to do. This is accessible to experts and non experts. A great read that goes beyond science and pure facts to explore the possibilities that our future holds. It might, just like Weapons of Math Destruction, leave you with a bitter aftertaste that Asimov put into words with such wisdom: The saddest aspect of life right now is that science gathers knowledge faster than society gathers wisdom.
What do you think about this list?
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