Whether you are teacher, student, computer scientist, or proficient machine learning programmer, there are many times where having a solid reference library on the topic can save you a lot of time and help you to prepare material for your next lecture, article, even job interview. Machine learning algorithms and lately, deep learning, have in fact demonstrated excellent results and produced many breakthroughs in computer science. This is revolutionising many fields, including healthcare where medical records, medical images, and other patient-specific information are combined with advanced machine learning approaches to create advanced algorithms capable of performing high levels of data mining and leveraging patient treatment. That being said, there is a lot of hype about Machine Learning, Artificial Intelligence (A.I), wrong expectations about what A.I is, and what it can actually do. While the Internet is full with resources about the topic, there is nothing better than learning from leading researchers and educators in the topic. This motivated me to create a list of recommended books from different authors, who also provide a different view and focus to the topic, as well as describing challenges and future opportunities.
So here it is, my list of top seven books about machine/deep learning that I’d recommend you to definitely check out. This list includes general purpose books about machine and deep learning, as well as more specific ones focusing on machine and deep learning for medical imaging. Some very good ones focusing on implementational aspects with Python and Tensorflow, for those readers who look for practical examples and more hands-on focused learning.
I hope you like. Feel free to use the links below to know more details, and happy reading!
| Books that focus on Machine Learning and Deep Learning | |
  | Deep Learning with Python by Francois Chollet Fresh from the oven, this has been an expected book since the first chapters were made available for free online. The book is excellent and Francois Chollet did a great job at explaining difficult concepts. |
  | Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow, 2nd Edition by Sebastian Raschka, Vahid Mirjalili On its second, revised and improved edition, this book is an excellent teaching material, guiding the reader from basic to advanced topics using main Python libraries and TensorFlow. The book features a great balance between theory and practice, and it also useful to those working on industrial applications. |
  | Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems by Aurélien Géron This is hands down (almost pun intended) one of the best books out there if you want to learn by doing. Highly recommended book as it includes theory and practice, including many examples. |
  | Deep Learning (Adaptive Computation and Machine Learning series) by Ian Goodfellow, Yoshua Bengio, Aaron Courville Definitely a reference book in the topic of Deep Learning. Compared to the other books listed here, this one puts more emphasis on the maths than on the practical aspects. An excellent accompanying book to the more practically-oriented ones. |
| Books that focus on Machine Learning and Medical Image Computing and Analysis | |
  | Deep Learning for Medical Image Analysis by S. Kevin Zhou, Hayit Greenspan, Dinggang Shen (Editors) This is one of the first books focusing on theory and applications of deep learning for medical image computing. Its seventeen chapters, divided in five parts, describes state of the art approaches developed in medical image computing to solve problems dealing with object recognition, image segmentation, image parsing, image registration and synthesis, etc. Applications include a vast variety of image modalities, featuring the flexibility and power of deep learning techniques for medical image analysis. This book is specially oriented to medical image computing scientists who are entering the field of deep learning. Specially, the introductory chapters I and II are specially oriented to give the reader an introduction to neural networks and deep convolutional neural nets for computer vision. |
  | Medical Image Recognition, Segmentation and Parsing: Machine Learning and Multiple Object Approaches (The Elsevier and Miccai Society Book Series) 1st Edition by S. Kevin Zhou This book from Kevin Zhou nicely complements the ones listed above with methods and approaches for image parsing and recognition. It also includes techniques and methodologies developed outside the field of deep learning, giving the reader a different view that complements the more DL-focused books listed above, and hopefully motivates the reader to consider how previous concepts and ideas could be now complemented, integrated or adapted to work with modern machine learning technologies. |
  | Machine Learning and Medical Imaging (Elsevier and Miccai Society) by Guorong Wu, Dinggang Shen, Mert Sabuncu (Editors) Also from the Medical Image Computing and Computer Assisted Community (MICCAI), this book nicely presents state of the art approaches in machine learning, going from classic machine learning approaches to fundamentals of deep learning. On a second part, the book presents a plethora of applications featuring different anatomical regions and image modalities, including even applications where genetic information is used. The book definitely provides a good overview of challenges and opportunities that machine learning has for medical imaging. |