Advanced Natural Language Processing with TensorFlow 2: Build real-world effective NLP applications using NER, RNNs, seq2seq models, Transformers, and
商品資訊
商品簡介
One-stop solution for NLP practitioners, ML developers and data scientists to build effective NLP systems that can perform real-world complicated tasks
Key Features
- Implement deep learning algorithms such as BiLSTMS, CRFs, and many more using TensorFlow 2
- Explore classical NLP techniques and libraries including parts-of-speech tagging and tokenization
- Learn practical applications of NLP covering the forefronts of the field like sentiment analysis and generating text
Book Description
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.
Further, it covers practical techniques that can simplify the labelling of textual data which otherwise proves to be a costly affair. The book also has a working code for each tech piece so that you can adapt them to your use cases.
By the end of this TensorFlow book, you will have an advanced knowledge of the tools, techniques and deep learning architecture used to solve complex NLP problems.
What You Will Learn
- Grasp important pre-steps in building NLP applications like POS tagging
- Deal with vast amounts of unlabeled and small labelled Datasets in NLP
- Use transfer and weakly supervised learning using libraries like Snorkel
- Perform sentiment analysis using BERT
- Apply encoder-decoder NN architectures and beam search for summarizing text
- Use transformer models with attention to bring images and text together
- Build applications that generate captions and answer questions about images
- Use advanced TensorFlow techniques like learning rate annealing, custom layers, and custom loss functions to build the latest deep NLP models
Who this book is for
This is not an introductory book and assumes the reader is familiar with basics of NLP and has fundamental Python skills, as well as basic knowledge of machine learning and undergraduate-level calculus and linear algebra.
The readers who can benefit the most from this book include:
Intermediate ML developers who are familiar with the basics of supervised learning and deep learning techniques
Professionals who already use TensorFlow/Python for purposes such as data science, ML, research, and analysis
主題書展
更多書展本週66折
您曾經瀏覽過的商品
購物須知
外文書商品之書封,為出版社提供之樣本。實際出貨商品,以出版社所提供之現有版本為主。部份書籍,因出版社供應狀況特殊,匯率將依實際狀況做調整。
無庫存之商品,在您完成訂單程序之後,將以空運的方式為你下單調貨。為了縮短等待的時間,建議您將外文書與其他商品分開下單,以獲得最快的取貨速度,平均調貨時間為1~2個月。
為了保護您的權益,「三民網路書店」提供會員七日商品鑑賞期(收到商品為起始日)。
若要辦理退貨,請在商品鑑賞期內寄回,且商品必須是全新狀態與完整包裝(商品、附件、發票、隨貨贈品等)否則恕不接受退貨。