Advanced Natural Language Processing with TensorFlow 2

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

• 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

Book Description

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

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