The Best Natural Language Processing eBooks for Beginners

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 2Explore classical NLP techniques and libraries including parts-of-speech tagging and tokenizationLearn 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 taggingDeal with vast amounts of unlabeled and small labelled Datasets in NLPUse transfer and weakly supervised learning using libraries like SnorkelPerform sentiment analysis using BERTApply encoder-decoder NN architectures and beam search for summarizing textUse transformer models with attention to bring images and text togetherBuild applications that generate captions and answer questions about imagesUse 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.

Conquer the labyrinth of natural language processing in artificial intelligence in no time, even if you’re a novice in the world of Machines and Codes!Have you ever wondered how Google translates languages with such precision? Or how your voice assistants are able to comprehend your commands and deliver accurate results? Are you fascinated by the intersection of linguistics and Artificial Intelligence (AI) but feel daunted by the complexities of learning a whole new field? Does the idea of machines understanding human language appear too far-fetched to fathom?

If you answered "yes" to any of these questions, it is time to bridge the gap between your aspirations and reality. Exploring the world of AI and Natural Language Processing (NLP) might seem like a tough challenge at first, but with the right guidance, it turns into a journey marked by self-discovery, curiosity, and the thrill of learning something new. Dive deep into the secrets of AI and NLP with the help of this insightful guide that will dispel the myths and take you on a journey you would've never believed was possible. Here's a tiny fraction of what you'll unveil in this revolutionary guide:. Unravel the mystery behind Natural Language Processing: Learn the integral components, evolutionary history, and immense potential it holdsDive into the 5 main stages of NLP and gain insights into how they interact within an AI systemUnderstand the role of Machine Learning in NLP and how it revolutionizes human-machine interactionGet acquainted with the [X] key algorithms used in NLP and their applications in the vast field of AIDiscover the [X] essential Python libraries and packages needed to create an effective NLP systemStep-by-step strategies for developing your own language model, with real-world examples to guide youExplore the pioneering research and emerging trends in the field of NLP and AILearn about the revolutionizing impact of Deep Learning on NLP and its practical implementationsUncover practical use cases of AI and NLP in various industries: From healthcare and e-commerce to customer service and marketingNavigate through the ethical considerations and challenges in implementing NLP in AI systemsAn insight into the opportunities and future prospects in the world of AI and NLPA special emphasis on the role and impact of NLP in Robotics, with a glimpse into the ongoing advancementsPractical, hands-on exercises and tasks to help reinforce your knowledge and skillsA sneak peek into the roadmap of building a successful career in AI and NLPA comprehensive guide on utilizing available resources and channels for continuous learning and skill enhancementAnd much, much more... Packed with practical examples, easy-to-follow guides, and real-world applications, this book is your ultimate guide into the groundbreaking field of AI and NLP. It ensures you walk away with actionable strategies and a clear roadmap to kickstart or enhance your journey in the AI landscape. You might wonder if this book is another technical manual filled with jargon that requires advanced knowledge in computer science to understand. Absolutely not! One of its distinguishing factors is its simplicity and clarity. The book does not assume previous knowledge or expertise in the field. Instead, it starts from the basics, gradually builds on them, and takes you through the complexities in an easily comprehensible manner.

Kickstart your NLP journey by exploring BERT and its variants such as ALBERT, RoBERTa, DistilBERT, VideoBERT, and more with Hugging Face's transformers library. Key Features. Explore the encoder and decoder of the transformer modelBecome well-versed with BERT along with ALBERT, RoBERTa, and DistilBERTDiscover how to pre-train and fine-tune BERT models for several NLP tasks. Book Description. BERT (bidirectional encoder representations from transformer) has revolutionized the world of natural language processing (NLP) with promising results.

This book is an introductory guide that will help you get to grips with Google's BERT architecture. With a detailed explanation of the transformer architecture, this book will help you understand how the transformer's encoder and decoder work. You'll explore the BERT architecture by learning how the BERT model is pre-trained and how to use pre-trained BERT for downstream tasks by fine-tuning it for NLP tasks such as sentiment analysis and text summarization with the Hugging Face transformers library. As you advance, you'll learn about different variants of BERT such as ALBERT, RoBERTa, and ELECTRA, and look at SpanBERT, which is used for NLP tasks like question answering. You'll also cover simpler and faster BERT variants based on knowledge distillation such as DistilBERT and TinyBERT. The book takes you through MBERT, XLM, and XLM-R in detail and then introduces you to sentence-BERT, which is used for obtaining sentence representation. Finally, you'll discover domain-specific BERT models such as BioBERT and ClinicalBERT, and discover an interesting variant called VideoBERT. By the end of this BERT book, you'll be well-versed with using BERT and its variants for performing practical NLP tasks. What You Will Learn. Understand the transformer model from the ground upFind out how BERT works and pre-train it using masked language model (MLM) and next sentence prediction (NSP) tasksGet hands-on with BERT by learning to generate contextual word and sentence embeddingsFine-tune BERT for downstream tasksGet to grips with ALBERT, RoBERTa, ELECTRA, and SpanBERT modelsGet the hang of the BERT models based on knowledge distillationUnderstand cross-lingual models such as XLM and XLM-RExplore Sentence-BERT, VideoBERT, and BART. Who this book is for. This book is for NLP professionals and data scientists looking to simplify NLP tasks to enable efficient language understanding using BERT. A basic understanding of NLP concepts and deep learning is required to get the best out of this book.