Learning machine learning, deep learning, data analysis, purpose or use, natural language understanding and processing is a very important research direction.
It should be said that not many neural networks for natural language processing such books, perhaps because of their own problems based?
In the learning process, read "natural language processing based on the depth of learning" books and "Python Natural Language Processing combat core technology and algorithms" e-book.
"Based on natural language processing depth study of" e-books, watching tagging, really put it down, basic knowledge is relatively new and some of the applications, there is more systematic, and highly recommended. "Based on natural language processing depth learning" focuses on the application of neural network model in natural language processing. First introduced and supervised machine learning the basics of feedforward neural networks, machine learning how to apply natural language processing, and the word vector representation (rather than notation) applications. Then more specifically described neural network structure, comprising a one-dimensional convolution neural network, recurrent neural network, the model and the model generating conditions based on attention. Clearly explain some of the more transparent, this area is not very good to write, to write such a thoughtful estimated only this book.
I think most alumni want to deal with the Chinese, we recommend a lot to learn "Python Natural Language Processing technology and real core algorithm."
"Python Natural Language Processing combat core technology and algorithms," e-book, focusing on the Chinese natural language processing, and related to Python framework as a tool to combat oriented, detailed account of the variety of natural language processing core technology, methodology and classical algorithm. Applications are doing by watching labeling, test code, mastered the sentiment analysis related concepts, scenes and processes generally do sentiment analysis, sentiment analysis in many industries.
Now two while watching e-books, take notes, debug code, learning NLP to some common depth learning algorithms, although these methods are more complicated point, but very practical, one can learn to use, on the other hand can also be useful for writing papers.
Organize their own collection of natural language processing, learning in depth study of electronic data we can also refer to:
https://www.yuque.com/baibinng/ctyewg/nhzqih
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