Master the challenging issues of natural language processing with cutting-edge deep learning techniques. This book explores NLP and deep learning fundamentals, including one-hot text representations, word embeddings, and models for textual similarity. Discover how to implement sequential NLP, deep memory-based NLP, and understand linguistic structure and hyperparameters for deep NLP. Learn to create advanced NLP applications using Python and Keras, and gain hands-on experience with state-of-the-art tools like BERT and XLNET. Ideal for those with intermediate Python skills and general NLP knowledge. Key topics covered: - Deep learning for NLP basics - Text embeddings - Textual similarity - Sequential NLP - Episodic memory for NLP - Attention mechanisms - Multitask learning - Transformers and their applications This book provides practical insights and best practices for building human-level AI systems that can comprehend and generate language. Summary: Deep Learning for Natural Language Processing by Stephan Raaijmakers, published by Manning Publications. This paperback book in English explores advanced NLP techniques with deep learning, covering essential concepts, models, and applications for developers.