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Key benefits

Apply deep learning algorithms and techniques such as BiLSTMS, CRFs, BPE and more using TensorFlow 2

Explore applications like text generation, summarization, weakly supervised labelling and more

Read cutting edge material with seminal papers provided in the GitHub repository with full working code

Description

Recently, there have been tremendous advances in NLP, and we are now moving from research labs into practical applications. This book comes with a perfect blend of both the theoretical and practical aspects of trending and complex NLP techniques. The book is focused on innovative applications in the field of NLP, language generation, and dialogue systems. It helps you apply the concepts of pre-processing text using techniques such as tokenization, parts of speech tagging, and lemmatization using popular libraries such as Stanford NLP and SpaCy. You will build Named Entity Recognition (NER) from scratch using Conditional Random Fields and Viterbi Decoding on top of RNNs. 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 chatbots. You will learn how to apply transfer learning and fine-tuning using TensorFlow 2. Further, it covers practical techniques that can simplify the labelling of textual data. The book also has a working code that is adaptable to your use cases for each tech piece. By the end of the book, you will have an advanced knowledge of the tools, techniques and deep learning architecture used to solve complex NLP problems.

Who is this book 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 and professionals who already use TensorFlow/Python for purposes such as data science, ML, research, analysis, etc.

What you will learn

Grasp important pre-steps in building NLP applications like POS tagging

Use transfer and weakly supervised learning using libraries like Snorkel

Do sentiment analysis using BERT

Apply encoder-decoder NN architectures and beam search for summarizing texts

Use Transformer models with attention to bring images and text together

Build apps that generate captions and answer questions about images using custom Transformers

Use advanced TensorFlow techniques like learning rate annealing, custom layers, and custom loss functions to build the latest DeepNLP models

What do you get with Print?

Instant access to your digital eBook copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Download this book in EPUB and PDF formats

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DRM FREE - Read whenever, wherever and however you want

AI Assistant (beta) to help accelerate your learning

Frequently bought together

Advanced Natural Language Processing with TensorFlow 2

€32.99

Python Natural Language Processing Cookbook

€41.99

Transformers for Natural Language Processing

€67.99

Total € 142.97

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Amazon verified reviews

Noopur Sethi Jun 06, 2021

Easy read - very helpful

Amazon Verified review

NJ Jun 05, 2021

This is a great book for everyone to read who want to pursue NLP using TensorFlow.

Tushar Kant Apr 10, 2021

I had a chance to read this book on NLP (Natural Language Processing) by Ashish Bansal. The most unique feature of this book is that it is a rare combination of deep theoretical insights (supported by both seminal and state-of-art papers in the domain) and practical implementation with code that may be easily downloaded from Github. The book may be used as primary source of knowledge as well as reference by people across industry & academia.I have personally learnt a lot from the chapters related to semi-supervised learning using GPT, Transformers etc. as well as Multi-Modal Networks. The beauty of the book lies in it's simplicity & eloquence in explaining very complex concepts in a simple & easy to understand manner. Our best wishes to Ashish for the success of this book and hope that the book becomes a de facto reference from industry professionals and teaching guide for people in academics.

Sanjib Basu Feb 20, 2021

If you are looking for an advanced book on NLP, full of examples and codes, please try this book. Starting from basic concepts of algorithms, deep learning, to using the latest libraries, advanced techniques, the book proves to be very useful. The descriptions are to the point, carrying little fuss on personal opinions, decorative illustrations, and literal analogies. In other words, the book is concise to compress the vast field of NLP knowledge.

Manu Goyal Jun 02, 2021

A practical book for techniques in NLP.The best part is the sample code included with the book that makes it easy to test.A word of caution - not very suitable for beginners.Overall 5*!

Advanced Natural Language Processing with TensorFlow 2: Build effective real-world NLP applications using NER, RNNs, seq2seq models, Transformers, and more

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