|Number of Pages||404 Pages|
|Dimensions (Cms)||19.05 x 2.31 x 23.5|
Learn how to redesign NLP applications from scratch. Key Features Get familiar with the basics of any Machine Learning or Deep Learning application. Understand how does preprocessing work in NLP pipeline. Use simple PyTorch snippets to create basic building blocks of the network commonly used in NLP. Get familiar with the advanced embedding technique, Generative network, and Audio signal processing techniques. Description 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.