Description
“Deep Learning Models for NLP” is an advanced-level textbook that explores the integration of deep learning techniques in natural language processing tasks. The book covers fundamental NLP concepts along with modern architectures such as recurrent neural networks (RNNs), long short-term memory (LSTM) networks, convolutional neural networks (CNNs), and transformer-based models. It highlights key applications including text classification, sentiment analysis, machine translation, and language modeling. With a balanced focus on theory and implementation, the text provides practical insights into handling textual data, feature representation using word embeddings, and model optimization techniques. Designed for students, researchers, and professionals, this book serves as a valuable resource for understanding and building intelligent language-based systems using deep learning approaches.
