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Deep Learning for Natural Language Processing
We are awash with text, from books, papers, blogs, tweets, news, and increasingly text from
spoken utterances. Every day, I get questions asking how to develop machine learning models
for text data. Working with text is hard as it requires drawing upon knowledge from diverse
domains such as linguistics, machine learning, statistical natural language processing, and these
days, deep learning.
I have done my best to write blog posts to answer frequently asked questions on the topic
and decided to pull together my best knowledge on the matter into this book. I designed this
book to teach you step-by-step how to bring modern deep learning methods to your natural
language processing projects. I chose the programming language, programming libraries, and
tutorial topics to give you the skills you need.
Python is the go-to language for applied machine learning and deep learning, both in terms
of demand from employers and employees. This is not least because it could be a renaissance
for machine learning tools. I have focused on showing you how to use the best of breed Python
tools for natural language processing such as Gensim and NLTK, and even a little scikit-learn.
Key to getting results is speed of development, and for this reason, we use the Keras deep
learning library as you can define, train, and use complex deep learning models with just a few
lines of Python code.
There are three key areas that you must know when working with text:
1. How to clean text. This includes loading, analyzing, filtering and cleaning tasks required
prior to modeling.
2. How to represent text. This includes the classical bag-of-words model and the modern
and powerful distributed representation in word embeddings.
3. How to generate text. This includes the range of most interesting problems, such as image
captioning and translation.
These key topics provide the backbone for the book and the tutorials you will work through.
I believe that after completing this book, you will have the skills t
2018-11-25
Dee learning with applications using Python
深度学习的实践 deep Learning has come a really long way. From the birth of the idea to understand human mind and the concept of associationism — how we
perceive things and how relationships of objects and views influence our
thinking and doing, to the modelling of associationism which started in
the 1870s when Alexander Bain introduced the first concert of Artificial
Neural Networks by grouping the neurons.
Fast forward it to today 2018 and we see how Deep Learning has
dramatically improved and is in all forms of life — from object detection,
speech recognition, machine translation, autonomous vehicles, face
detection and the use of face detection from mundane tasks such as
unlocking your iPhoneX to doing more profound tasks such as crime
detection and prevention
2018-05-29
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