LEADER 03476nam 22005895 450 001 9910483388203321 005 20230804142434.0 010 $a1-5231-5076-9 010 $a1-4842-6664-1 024 7 $a10.1007/978-1-4842-6664-9 035 $a(CKB)4100000011716929 035 $a(DE-He213)978-1-4842-6664-9 035 $a(MiAaPQ)EBC6455456 035 $a(CaSebORM)9781484266649 035 $a(PPN)25325664X 035 $a(EXLCZ)994100000011716929 100 $a20210112d2021 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aHands-on Question Answering Systems with BERT $eApplications in Neural Networks and Natural Language Processing /$fby Navin Sabharwal, Amit Agrawal 205 $a1st ed. 2021. 210 1$aBerkeley, CA :$cApress :$cImprint: Apress,$d2021. 215 $a1 online resource (XV, 184 p. 80 illus.) 300 $aIncludes index. 311 $a1-4842-6663-3 327 $aChapter 1: Introduction to Natural Language Processing -- Chapter 2: Introduction to Word Embeddings -- Chapter 3: BERT Algorithms Explained -- Chapter 4: BERT Model Applications - Question Answering System -- Chapter 5: BERT Model Applications - Other tasks -- Chapter 6: Future of BERT models. 330 $aGet hands-on knowledge of how BERT (Bidirectional Encoder Representations from Transformers) can be used to develop question answering (QA) systems by using natural language processing (NLP) and deep learning. The book begins with an overview of the technology landscape behind BERT. It takes you through the basics of NLP, including natural language understanding with tokenization, stemming, and lemmatization, and bag of words. Next, you?ll look at neural networks for NLP starting with its variants such as recurrent neural networks, encoders and decoders, bi-directional encoders and decoders, and transformer models. Along the way, you?ll cover word embedding and their types along with the basics of BERT. After this solid foundation, you?ll be ready to take a deep dive into BERT algorithms such as masked language models and next sentence prediction. You?ll see different BERT variations followed by a hands-on example of a question answering system. Hands-on Question Answering Systems with BERT is a good starting point for developers and data scientists who want to develop and design NLP systems using BERT. It provides step-by-step guidance for using BERT. You will: Examine the fundamentals of word embeddings Apply neural networks and BERT for various NLP tasks Develop a question-answering system from scratch Train question-answering systems for your own data. 606 $aMachine learning 606 $aCloud Computing 606 $aProgramming languages (Electronic computers) 606 $aMachine Learning 606 $aCloud Computing 606 $aProgramming Language 615 0$aMachine learning. 615 0$aCloud Computing. 615 0$aProgramming languages (Electronic computers). 615 14$aMachine Learning. 615 24$aCloud Computing. 615 24$aProgramming Language. 676 $a006.32 700 $aSabharwal$b Navin$0911357 702 $aAgrawal$b Amit 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bUtOrBLW 906 $aBOOK 912 $a9910483388203321 996 $aHands-on question answering systems with BERT$92849429 997 $aUNINA