Thirteen case studies with code, data, and configurations across different approaches for NLP and Speech recognition tasks such as Embeddings, Classification, Distributed Representation, Summarization, Machine Translation, Sentiment Analysis, Cross Domain Transfer Learning, Multi-Task NLP, End to End Speech, and Question Answering Practical aspects of using these techniques with tips and tricks essential for real-world applicationsĪ hands-on approach to using Python-based deep learning libraries such as Keras, TensorFlow, and PyTorch to apply these techniques in the context of real-world case studies A comprehensive resource that builds up from elementary deep learning, text, and speech principles to advanced state-of-the-art neural architecturesĪ ready reference for deep learning techniques applicable to common NLP and speech recognition applicationsĪ useful resource on successful architectures and algorithms with essential mathematical insights explained in detailĪn in-depth reference and comparison of the latest end-to-end neural speech processing approachĪ panoramic resource on leading edge transfer learning, domain adaptation and deep reinforcement learning architectures for text and speech

Deep learning for nlp