{"product_id":"transformers-for-natural-language-processing-build-innovative-deep-neural-network-architectures-for-nlp-with-python-pytorch-tensorflow-bert-rober-paperback","title":"Transformers for Natural Language Processing: Build innovative deep neural network architectures for NLP with Python, PyTorch, TensorFlow, BERT, RoBER - Paperback","description":"\u003cp\u003eby \u003cb\u003eDenis Rothman\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eTake your NLP knowledge to the next level and become an AI language understanding expert by mastering the quantum leap of Transformer neural network models\u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eKey Features\u003c\/strong\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eBuild and implement state-of-the-art language models, such as the original Transformer, BERT, T5, and GPT-2, using concepts that outperform classical deep learning models\u003c\/li\u003e\n\u003cli\u003eGo through hands-on applications in Python using Google Colaboratory Notebooks with nothing to install on a local machine\u003c\/li\u003e\n\u003cli\u003e Test transformer models on advanced use cases\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eBook Description\u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003eThe transformer architecture has proved to be revolutionary in outperforming the classical RNN and CNN models in use today. With an apply-as-you-learn approach, Transformers for Natural Language Processing investigates in vast detail the deep learning for machine translations, speech-to-text, text-to-speech, language modeling, question answering, and many more NLP domains with transformers.\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003eThe book takes you through NLP with Python and examines various eminent models and datasets within the transformer architecture created by pioneers such as Google, Facebook, Microsoft, OpenAI, and Hugging Face.\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003eThe book trains you in three stages. The first stage introduces you to transformer architectures, starting with the original transformer, before moving on to RoBERTa, BERT, and DistilBERT models. You will discover training methods for smaller transformers that can outperform GPT-3 in some cases. In the second stage, you will apply transformers for Natural Language Understanding (NLU) and Natural Language Generation (NLG). Finally, the third stage will help you grasp advanced language understanding techniques such as optimizing social network datasets and fake news identification.\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003eBy the end of this NLP book, you will understand transformers from a cognitive science perspective and be proficient in applying pretrained transformer models by tech giants to various datasets.\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eWhat You Will Learn\u003c\/strong\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eUse the latest pretrained transformer models\u003c\/li\u003e\n\u003cli\u003eGrasp the workings of the original Transformer, GPT-2, BERT, T5, and other transformer models\u003c\/li\u003e\n\u003cli\u003eCreate language understanding Python programs using concepts that outperform classical deep learning models\u003c\/li\u003e\n\u003cli\u003eUse a variety of NLP platforms, including Hugging Face, Trax, and AllenNLP\u003c\/li\u003e\n\u003cli\u003eApply Python, TensorFlow, and Keras programs to sentiment analysis, text summarization, speech recognition, machine translations, and more\u003c\/li\u003e\n\u003cli\u003eMeasure the productivity of key transformers to define their scope, potential, and limits in production\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eWho this book is for\u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003eSince the book does not teach basic programming, you must be familiar with neural networks, Python, PyTorch, and TensorFlow in order to learn their implementation with Transformers.\u003c\/p\u003e\u003cp\u003eReaders who can benefit the most from this book include experienced deep learning \u0026amp; NLP practitioners and data analysts \u0026amp; data scientists who want to process the increasing amounts of language-driven data.\u003c\/p\u003e\u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 384\u003c\/div\u003e\u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 0.79 x 9.25 x 7.5 IN\u003c\/div\u003e\u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e January 28, 2021\u003c\/div\u003e","brand":"Books by splitShops","offers":[{"title":"Default Title","offer_id":47412220494002,"sku":"9781800565791","price":143.98,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0770\/3891\/1666\/files\/19784eab074d8672752b89e5b2c6c381.webp?v=1778316504","url":"https:\/\/box.dadyminds.org\/products\/transformers-for-natural-language-processing-build-innovative-deep-neural-network-architectures-for-nlp-with-python-pytorch-tensorflow-bert-rober-paperback","provider":"DADYMINDS BOX","version":"1.0","type":"link"}