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 that you need to both work through your own natural language processing projects and bring modern deep learning methods to bare

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