The Best Computational Linguistics Books of All Time

OpenAI's GPT-3, ChatGPT, GPT-4 and Hugging Face transformers for language tasks in one book. Get a taste of the future of transformers, including computer vision tasks and code writing and assistance. Purchase of the print or Kindle book includes a free eBook in PDF format. Key Features:. Pretrain a BERT-based model from scratch using Hugging FaceFine-tune powerful transformer models, including OpenAI's GPT-3, to learn the logic of your dataPerform root cause analysis on hard NLP problems. Book Description:. Transformers are...well...transforming the world of AI.

There are many platforms and models out there, but which ones best suit your needs? Transformers for Natural Language Processing, 2nd Edition, guides you through the world of transformers, highlighting the strengths of different models and platforms, while teaching you the problem-solving skills you need to tackle model weaknesses. You'll use Hugging Face to pretrain a RoBERTa model from scratch, from building the dataset to defining the data collator to training the model. If you're looking to fine-tune a pretrained model, including GPT-3, then Transformers for Natural Language Processing, 2nd Edition, shows you how with step-by-step guides. The book investigates machine translations, speech-to-text, text-to-speech, question-answering, and many more NLP tasks. It provides techniques to solve hard language problems and may even help with fake news anxiety (read chapter 13 for more details). You'll see how cutting-edge platforms, such as OpenAI, have taken transformers beyond language into computer vision tasks and code creation using Codex. By the end of this book, you'll know how transformers work and how to implement them and resolve issues like an AI detective! What You Will Learn:. Find out how ViT and CLIP label images (including blurry ones!) and create images from a sentence using DALL-EDiscover new techniques to investigate complex language problemsCompare and contrast the results of GPT-3 against T5, GPT-2, and BERT-based transformersCarry out sentiment analysis, text summarization, casual speech analysis, machine translations, and more using TensorFlow, PyTorch, and GPT-3Measure the productivity of key transformers to define their scope, potential, and limits in production. Who this book is for:. If you want to learn about and apply transformers to your natural language (and image) data, this book is for you. You'll need a good understanding of Python and deep learning and a basic understanding of NLP to benefit most from this book. Many platforms covered in this book provide interactive user interfaces, which allow readers with a general interest in NLP and AI to follow several chapters. And, don't worry if you get stuck or have questions; this book gives you direct access to our AI/ML community and author, Denis Rothman. So, he'll be there to guide you on your transformers journey!

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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.