|AI Books To Inspire You|
|Written by Kay Ewbank|
|Monday, 30 November 2020|
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Artificial Intelligence is an increasingly important subject for developers, and the books included in this bookshelf represent the best that we've covered in areas including machine learning, reinforcement learning, deep learning and neural networks and natural language processing.
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Python Machine Learning, 3rd Ed
Authors: Sebastian Raschka and Vahid Mirjalili
Mike James concluded that this is a good book on AI if you want to work in Python. It isn't focused on neural networks, but the new edition extends the coverage to GANs and reinforcement learning. If you want a book entirely on neural networks then this isn't it - but Mike's advice is to guard against being so narrow.
Overall it's a clear and practical introduction to modern machine learning with just enough math to make sure you know what is going on and enough basic explanation if you are having trouble with the math.
Machine Learning in Action
Author: Peter Harrington
This particular book gets a good balance between advanced topics and practical application, according to Mike James who awarded it 4.5 stars, noting that all of the examples are in Python which isn't a bad choice of a language because it has many handy libraries that will do the computation for you.
Mike's conclusion is that this isn't a complete survey of machine learning but it isn't a bad summary of machine learning as it is commonly used in building more intelligent web sites, and if you are interested in applied machine learning, and are happy working in Python, then just go and buy a copy of this book. It won't be the last book on the subject you will need, but it will be one that more than gets you started and you will return to it in the future.
Machine Learning: An Algorithmic Perspective
Author: Stephen Marsland
The book takes a fairly traditional approach to AI, and Mike James awarded it 4.5 stars. He says that given that it isn't a huge book it can't cover everything and in particular Expert Systems are notable by their absence.
Mike says that this is an academic text in the sense that it covers the subject matter of many an introductory course on AI and it has references to the source material and further reading - but it is written in a fairly casual style. Overall it works and much of the mathematics is explained in ways that make it fairly clear what is going on and if you are in doubt about exactly what it all means there is always the Python code to read. He says this is a suitable introduction to AI if you are studying the subject on your own and it would make a good course text for an introduction and overview of AI.
|Last Updated ( Tuesday, 01 December 2020 )|