Advanced Natural Language Processing with TensorFlow 2: Build effective real-world NLP applications using NER, RNNs, seq2seq models, Transformers, and more by Ashish Bansal
Requirements: .ePUB reader, 9 MB
Overview: In the last couple of years, there have been tremendous advances in natural language processing, and we are now moving from research labs into practical applications. Advanced Natural Language Processing comes with a perfect blend of both the theoretical and practical aspects of trending and complex NLP techniques.
This book is focused on innovative applications in the field of NLP, language generation, and dialogue systems. It goes into the details of applying the concepts of text pre-processing using techniques such as tokenization, parts of speech tagging, and lemmatization using popular libraries such as Stanford NLP and SpaCy. Named Entity Recognition (NER), a cornerstone of task-oriented bots, is built from scratch using Conditional Random Fields and Viterbi Decoding on top of RNNs.
Taking a practical and application-focused perspective, the book covers key emerging areas such as generating text for use in sentence completion and text summarization, bridging images and text by generating captions for images, and managing dialogue aspects of chatbot design. It also covers one of the most important reasons behind recent advances in NLP – applying transfer learning and fine-tuning using TensorFlow 2.
Genre: Non-Fiction > Tech & Devices
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