Free eBooks on Artificial Intelligence to read in 2022 [UPDATED]

DLabs.AI

Artificial intelligence is an area where you must develop your skills regularly. As it’s a relatively new space, specialists are more than happy to share their knowledge; some even do it for free! If you’re looking for the best free eBooks related to artificial intelligence, machine learning, or deep learning – this list is for you. Here’s a rundown of our favourite free eBooks.

Please note: Our article is a list, not a ranking. We think each eBook is equally valuable, and we recommend you read each one.

1. 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 deep learning. 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.

2. Deep Learning

Authors: Ian Goodfellow, Yoshua Bengio, Aaron Courville

Let’s stick with the subject of Deep Learning. The authors created this next resource to help beginners enter the field of machine learning, with a focus on deep learning. Interestingly, one of the authors –  Yoshua Bengio won the 2018 Turing Award (the Nobel Prize of computing) for his work in deep learning.

The online version is available directly from their website. And just like the previous eBook, this one is also updated regularly — but to stay up-to-date with the latest research, you should also subscribe to their newsletter.

3. The Hundred-Page Machine Learning Book 

Author: Andriy Burkov

Are 100 pages enough to conquer machine learning? We’re not so sure, but we guarantee this free 100-page eBook will give you almost all of the essential information for beginners. And the author promises the book will teach you how machine learning works so that you’re ready to build complex AI systems, pass an interview, or start your own business. Don’t believe him? Give it a read and see for yourself!

It’s worth noting that the eBook is distributed on a “read first, buy later” principle. Meaning you can download it for free, and if you find it useful, you can pay for this resource.

4. Interpretable Machine Learning. A Guide for Making Black Box Models Explainable

Author: Christoph Molnar

If you’re looking to learn how to make machine learning decisions interpretable, this is the eBook for you! It details how to select and apply the best interpretation methods for any machine learning project — making it a valuable source of knowledge for data scientists, statisticians, machine learning engineers, and anyone interested in machine learning.

5. Python Data Science Handbook

Author: Jake VanderPlas

Python is a first-class tool for any data scientist. And with this book, you’ll learn how to use its most important tools, including IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and many others. This resource is perfect for tackling day-to-day issues such as cleaning, manipulating, and transforming data — or building machine learning models. 

The full eBook is available for free on GitHub. But if you find it useful, you might consider supporting the author’s work by buying the book here.

6. 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.

7. Deep Learning with PyTorch

Authors: Eli Stevens, Luca Antiga, Thomas Viehmann

If you’re planning to build neural networks with PyTorch, you’ll want to begin your journey with this popular, open-source machine learning framework. The eBook provides a great introduction to the subject, sharing practical knowledge related to pre-trained networks, how to use a neural network and convolutions, how to deploy a model to production, and much more. 

But move fast: the eBook is only available as a free download for a limited time.

8. An Introduction to Statistical Learning with Applications in R

Authors: Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani

Next up, we’re recommending the winner of the 2014 Eric Ziegel award from Technometrics. This eBook includes an introduction to statistical learning methods and several R labs, with elaborate explanations on how to implement particular methods in real-life settings. It’s an excellent resource for students — as well as practising data scientists looking to improve their skills.

9. Neural Networks and Deep Learning

Author: Michael Nielsen

If you want to learn about deep learning and neural networks, I highly recommend this resource. It will help you better understand the two topics. Then, show you how to build a deep neural network from scratch. It’s the perfect choice for beginners who want to obtain a robust grounding in the core principles of two complex subjects. The book has gained popularity primarily due to the chapter “A visual proof that neural nets can compute any function”, which shows a new, unconventional approach to this topic.

10. How to implement AI in your company

Authors: Przemysław Majewski, Katarzyna Rojewska, Emilia Brzozowska, Bertie Conibear 

The next ebook is dedicated to all businesses that are still considering implementing artificial intelligence. This is a free guide for all entrepreneurs who want to know key AI’s benefits or are not sure where to start with this technology. The paper provides simple explanations of all key AI concepts, the steps needed, insights and data from the latest industry reports, and many other helpful advice and suggestions.

11. 10 Machine Learning Frameworks to Try

Author: DLabs.AI Team

Do you know what Google, Spotify, Airbnb, and Coca-Cola have in common? They all use machine learning technology. So if you would like to get a deeper insight, you’re in a right place. Firstly, if you’re thinking of managing business issues with ML-powered solutions, you should be familiar with ML frameworks that can increase efficiency and decrease work time. And that’s the reason why this ebook is just for you. You’ll find there ten recommended ML frameworks to check. Besides descriptions, there are valuable tips on why to use a particular framework or when to avoid one. 

12. How to build AI-driven object detection software

Authors: Maciej Karpicz, Marek Orliński, Mariusz Rzepka, Michał Wojczulis

Finally, it’s only fitting to end with an eBook authored by the DLabs team. It stretches to 75 pages and is a treasure trove of knowledge drawn from our big data and machine learning experts. 

It includes step-by-step instructions on how to build object detection software using deep learning and synthetic data. And while we created it specifically for CTOs and tech leaders; if you’re a data scientist, programmer, or artificial intelligence aficionado — you’ll find plenty of valuable insights within.

13. 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.

14. A Brief Introduction to Neural Networks 

Author: David Kriesel

David Kriesel initially wrote this book for a seminar at the University of Bonn in Germany. But he quickly recognized the topic’s potential, so he shared it with the world. 

The best thing about the content is that it has and will always be extended, meaning the information will stay up-to-date. Each chapter also gives a profound insight into the paradigm of neural networks, including LATEX. 

The author himself designed the illustrations for the book, making them as easy to understand and as memorable as possible.

15. 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.

16. 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.

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DLabs.AI is a team of Data Science experts providing comprehensive solutions and IT systems along with Machine Learning and Artificial Intelligence algorithms that maximize business clients'​ profits and minimize the risks associated with implementation.

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