{"id":1568,"date":"2019-01-13T18:00:43","date_gmt":"2019-01-13T18:00:43","guid":{"rendered":"https:\/\/www.aiproblog.com\/index.php\/2019\/01\/13\/three-must-own-books-for-deep-learning-practitioners\/"},"modified":"2019-01-13T18:00:43","modified_gmt":"2019-01-13T18:00:43","slug":"three-must-own-books-for-deep-learning-practitioners","status":"publish","type":"post","link":"https:\/\/www.aiproblog.com\/index.php\/2019\/01\/13\/three-must-own-books-for-deep-learning-practitioners\/","title":{"rendered":"Three Must-Own Books for Deep Learning Practitioners"},"content":{"rendered":"

Author: Jason Brownlee<\/p>\n

\n

Developing neural networks is often referred to as a dark art.<\/p>\n

The reason for this is that being skilled at developing neural network models comes from experience. There are no reliable methods to analytically calculate how to design a \u201cgood\u201d or \u201cbest\u201d model for your specific dataset. You must draw on experience and experiment in order to discover what works on your problem.<\/p>\n

A lot of this experience can come from actually developing neural networks on test problems.<\/p>\n

Nevertheless, many people have come before and recorded their discoveries, best practices, and preferred techniques. You can learn a lot about how to design and configure neural networks from some of the best books on the topic.<\/p>\n

In this post, you will discover the three books that I recommend reading and having next to you when developing neural networks for your datasets.<\/p>\n

Let\u2019s get started.<\/p>\n

Three Recommended Books on Neural Networks<\/h2>\n

There are three books that I think you must own physical copies<\/em> of if you are a neural network practitioner.<\/p>\n

They are:<\/p>\n

    \n
  1. Neural Networks for Pattern Recognition<\/a>, 1995.<\/li>\n
  2. Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks<\/a>, 1999.<\/li>\n
  3. Deep Learning<\/a>, 2016.<\/li>\n<\/ol>\n

    These books are references, not tutorials.<\/p>\n

    You dip into them again and again before and during projects to ensure that you are getting everything you can out of your data and models.<\/p>\n

    These are the books that I read and reference all the time. If you have books that you recommend when developing neural network models, please let me know in the comments below.<\/p>\n

    Now, let\u2019s take a closer look at each book in turn.<\/p>\n

    Neural Networks for Pattern Recognition<\/h2>\n

    Neural Networks for Pattern Recognition<\/a> by Christopher Bishop<\/a> was released in 1995.<\/p>\n

    \"Neural<\/a><\/p>\n

    Neural Networks for Pattern Recognition<\/p>\n<\/div>\n

    This great book was followed about a decade later by the still classic textbook Pattern Recognition and Machine Learning<\/a> (fondly referred to as PRML). Christopher Bishop is both a professor at the University of Edinburgh and a director at Microsoft\u2019s Cambridge research lab<\/a>.<\/p>\n

    This book is a classic in the field of neural networks. It is a handbook that handily captures both the state of theory at the time, and techniques that remain just as relevant today nearly 25 years later.<\/p>\n

    Although reading the book cover to cover will provide you a robust foundation, I\u2019d instead encourage you to use it as a reference for getting the most out of your neural network models.<\/p>\n

    I\u2019d recommend dipping into the following chapters as needed:<\/p>\n