As you can see from the preceding results, our translation model is not behaving ideally. These results were obtained by running the optimization for more than 12 hours on a single NVIDIA 1080 Ti GPU. Also note that this is not even the full dataset, we only used 250,000 sentence pairs for training. However, if you type something into Google Translate, which uses the Google Neural Machine Translation (GNMT) system, the translation almost always looks very realistic with only minor mistakes. So it is important to know how we can improve the model so that it can produce better results. In this section, we will discuss several ways of improving NMTs such as teacher forcing, deep LSTMs, and attention mechanism.
Natural Language Processing with TensorFlow
By :
Natural Language Processing with TensorFlow
By:
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