Here we discussed some of the mathematical background as well as some implementations we did not cover in the other sections. First we discussed the mathematical notation for scalars, vectors, matrices and tensors. Then we discussed various operations performed on these data structures, such as, matrix multiplication and inversion. Next, we discussed various terminology that is useful for understanding probabilistic machine learning such as, probability density functions, joint probability, marginal probability and Bayes rule. Afterwards, we moved our discussion to cover various implementations that we did not visit in the other chapters. We learnt how to use Keras; a high-level TensorFlow library to implement a CNN. Then we discussed how we can efficiently implement a neural machine translator with the seq2seq library in TensorFlow, compared to the implementation we discussed in Chapter 10, Sequence-to-Sequence Learning – Neural Machine Translation. Finally, we ended this section with a guide that teaches you to visualize word embeddings using the TensorBoard; a visualization platform that comes with TensorFlow.
Natural Language Processing with TensorFlow
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Natural Language Processing with TensorFlow
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Overview of this book
Natural language processing (NLP) supplies the majority of data available to deep learning applications, while TensorFlow is the most important deep learning framework currently available. Natural Language Processing with TensorFlow brings TensorFlow and NLP together to give you invaluable tools to work with the immense volume of unstructured data in today’s data streams, and apply these tools to specific NLP tasks.
Thushan Ganegedara starts by giving you a grounding in NLP and TensorFlow basics. You'll then learn how to use Word2vec, including advanced extensions, to create word embeddings that turn sequences of words into vectors accessible to deep learning algorithms. Chapters on classical deep learning algorithms, like convolutional neural networks (CNN) and recurrent neural networks (RNN), demonstrate important NLP tasks as sentence classification and language generation. You will learn how to apply high-performance RNN models, like long short-term memory (LSTM) cells, to NLP tasks. You will also explore neural machine translation and implement a neural machine translator.
After reading this book, you will gain an understanding of NLP and you'll have the skills to apply TensorFlow in deep learning NLP applications, and how to perform specific NLP tasks.
Table of Contents (16 chapters)
Natural Language Processing with TensorFlow
Contributors
Preface
Free Chapter
Introduction to Natural Language Processing
Understanding TensorFlow
Word2vec – Learning Word Embeddings
Advanced Word2vec
Sentence Classification with Convolutional Neural Networks
Recurrent Neural Networks
Long Short-Term Memory Networks
Applications of LSTM – Generating Text
Applications of LSTM – Image Caption Generation
Sequence-to-Sequence Learning – Neural Machine Translation
Current Trends and the Future of Natural Language Processing
Mathematical Foundations and Advanced TensorFlow
Index
Customer Reviews