| AI Books To Inspire You |
| Written by Kay Ewbank | ||||||
| Monday, 30 November 2020 | ||||||
Page 2 of 2
Handbook of Natural Language Processing (2e)Author: Nitin Indurkhya & Fred J. Damerau (Editors) In looking for a Best Book selection in the Artificial Intelligence catagory, Mike James selected a highly readable collection of papers on Natural Language Processing. Awarding it 5 stars, Mike said that while you might guess that it is going to be another boring collection of difficult to read papers - only you would be wrong! If you need a readable introduction to this important subject - this is it.
This is a good way to get into NLP. You will probably need additional, more specialized, texts to guide your next steps but this does provide a basic course on the subject suitable both for academic and practical development. Highly recommended. Natural Language Processing with PythonAuthor: Steven Bird, Ewan Klein & Edward Loper Mike James says this book is likely to get you enthusiastic about language processing, and gave it 4.5 stars while remaining skeptical about whether this is a good thing. The Python NLTK Natural Language Toolkit is used to demonstrate practical natural language processing rather than theory., and the book starts from simple things - almost just text processing.
Mike's conclusion is that if you are thinking about adding natural language processing to any sort of application this is a must read book. It is also great fun and if have any interest in AI you will enjoy reading it. His caveat is that if you want to be reminded of how difficult the natural language problem is just read the Afterword. The sentences listed there are enough to make you realise that language is a wonderful invention. Advanced Deep Learning with TensorFlow 2 and Keras, 2nd EdAuthor: Rowel Atienza This is a book for developers wanting to master neural networks,and while it isn't an advanced theoretical text, it does offer a wide range of advanced examples, according to Mike James, who gave it a maximum five star rating. He says that the examples go well beyond the basic introductions to any of the topics that you will find in most other books, and is very strong on GANs and if this is an area that interests you then so much the better. Foundations of Deep Reinforcement LearningAuthors: Laura Graesser and Wah Loon Keng This book is excellent and Mike James, awarding it five stars, says that if you have any interest in reinforcement learning just buy it, read it and learn. It is a guide to the theory and the practice, and while it isn't an easy book to read and it will take you some time to actually read very much of it, this is because the subject matter is difficult and the book does its best to explain and motivate it.
Even so, as already stated, you are going to have to be happy reading quite complicated equations and understanding them. The only way that this could have been avoided is by not telling you how things work and this is not the intention of this book. Mike's conclusion is that this is an excellent book, and if you are in, or want to be in, the field you should read it. Also on Programmer's BookshelfGood Reads In Applied Programming Theory And Techniques Top Computing Theory Book Choices Reading Your Way Into Big Data 
Follow @bookwatchiprog on Twitter or subscribe to I Programmer's Books RSS feed for our new reviews and for each day's new addition to Book Watch and visit Book Watch Archive for hundreds more titles. You can also follow us on Facebook or sign up for our weekly newsletter.
<ASIN:1420085921> <ASIN:0596516495> <ASIN:1838821651>
|
||||||
| Last Updated ( Tuesday, 01 December 2020 ) |
