Getting started with Deep Learning for Natural Language Processing: Learn how to build NLP applications with Deep LearningКНИГИ » ПРОГРАММИНГ
Название: Getting started with Deep Learning for Natural Language Processing: Learn how to build NLP applications with Deep Learning Автор: Sunil Patel Издательство: BPB Publications Год: 2021 Формат: ePUB, PDF Страниц: 783 Размер: 14,8 Mb Язык: English
Natural language processing (NLP) is one of the areas where many Machine Learning and Deep Learning techniques are applied. This book covers wide areas, including the fundamentals of Machine Learning, Understanding and optimizing Hyperparameters, Convolution Neural Networks (CNN), and Recurrent Neural Networks (RNN). This book not only covers the classical concept of text processing but also shares the recent advancements. This book will empower users in designing networks with the least computational and time complexity. This book not only covers basics of Natural Language Processing but also helps in deciphering the logic behind advanced concepts/architecture such as Batch Normalization, Position Embedding, DenseNet, Attention Mechanism, Highway Networks, Transformer models and Siamese Networks. This book also covers recent advancements such as ELMo-BiLM, SkipThought, and Bert. This book also covers practical implementation with step by step explanation of deep learning techniques in Topic Modelling, Text Generation, Named Entity Recognition, Text Summarization, and Language Translation. In addition to this, very advanced and open to research topics such as Generative Adversarial Network and Speech Processing are also covered.
What will you learn ? Learn how to leveraging GPU for Deep Learning ? Learn how to use complex embedding models such as BERT ? Get familiar with the common NLP applications. ? Learn how to use GANs in NLP ? Learn how to process Speech data and implementing it in Speech applications
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Getting started with Deep Learning for Natural Language Processing: Learn how to build NLP applications with Deep Learning 
