 |
5 books on Natural Language Processing [PDF]
November 12, 2024 | 24 |
Books on Natural Language Processing (NLP) describe foundational theories, techniques, and new advancements in NLP, providing startups with the necessary knowledge to develop large language models, chatbots, sentiment analysis tools, language translation systems, and more.
1. Machine Learning and Deep Learning in Natural Language Processing
2023 by Anitha S. Pillai, Roberto Tedesco
From this book I've learned that conversational agents (chatbots) are a key application of NLP. For example chatbots can assist in speech restoration and Parkinson’s disease detection. NLP neural networks can also handle non-literal content such as emotions. In particular, emotion-reading text-to-speech models play a role in healthcare support, such as aiding psychotherapists.
Download PDF
2. ChatGPT for Enterprise: Using Generative AI to bring AI to business
2023 by Jothi Periasamy
This book explains practical use cases for ChatGPT in business applications and shows how ChatGPT can be integrated into corporate workflows to improve productivity. You'll get step-by-step guide for developing and deploying GPT models on Google Cloud Platform and links to GitHub resources are available. Generative AI can be applied across industries such as retail, energy and education.
Download PDF
3. Natural Language Processing: A Machine Learning Perspective
2021 by Yue Zhang, Zhiyang Teng
This book is for developers. It tells how NLP tasks like classification and sequence labeling can be approached with machine learning models. Supervised and unsupervised learning with latent variables can be applied in NLP. Machine learning-based approaches reduce the focus on linguistic rules in NLP. Transition-based techniques are helpful in structured prediction models. You'll also learn why statistical deep learning models are essential for text classification.
Download PDF
4. Natural Language Processing Projects: Build Next-Generation NLP Applications Using AI Techniques
2021 by Akshay Kulkarni, Adarsha Shivananda, Anoosh Kulkarni
From this book you'll figure out how sentiment analysis and topic modeling can be implemented using Python and deep learning. Contextual embeddings can help you to identify similar sentences in large datasets and LSTMs can be used for automatic word suggestions. The authors state that transfer learning is effective for building chatbots while recurrent neural networks are useful for document summarization.
Download PDF
5. Deep Learning for NLP and Speech Recognition
2019 by Uday Kamath, John Liu, James Whitaker
This book contains hands-on case studies that demonstrate the implementation of deep learning in speech recognition. It proves that deep learning models (that are used for better understanding of spoken words) are essential for speech recognition and machine translation. NLP-based speech recognition can be integrated into various industries like finance and healthcare. Practical applications of deep learning in NLP include document classification and voice analysis.
Download PDF
How to download PDF:
1. Install Google Books Downloader
2. Enter Book ID to the search box and press Enter
3. Click "Download Book" icon and select PDF*
* - note that for yellow books only preview pages are downloaded
1. Machine Learning and Deep Learning in Natural Language Processing
2023 by Anitha S. Pillai, Roberto Tedesco
From this book I've learned that conversational agents (chatbots) are a key application of NLP. For example chatbots can assist in speech restoration and Parkinson’s disease detection. NLP neural networks can also handle non-literal content such as emotions. In particular, emotion-reading text-to-speech models play a role in healthcare support, such as aiding psychotherapists.
Download PDF
2. ChatGPT for Enterprise: Using Generative AI to bring AI to business
2023 by Jothi Periasamy
This book explains practical use cases for ChatGPT in business applications and shows how ChatGPT can be integrated into corporate workflows to improve productivity. You'll get step-by-step guide for developing and deploying GPT models on Google Cloud Platform and links to GitHub resources are available. Generative AI can be applied across industries such as retail, energy and education.
Download PDF
3. Natural Language Processing: A Machine Learning Perspective
2021 by Yue Zhang, Zhiyang Teng
This book is for developers. It tells how NLP tasks like classification and sequence labeling can be approached with machine learning models. Supervised and unsupervised learning with latent variables can be applied in NLP. Machine learning-based approaches reduce the focus on linguistic rules in NLP. Transition-based techniques are helpful in structured prediction models. You'll also learn why statistical deep learning models are essential for text classification.
Download PDF
4. Natural Language Processing Projects: Build Next-Generation NLP Applications Using AI Techniques
2021 by Akshay Kulkarni, Adarsha Shivananda, Anoosh Kulkarni
From this book you'll figure out how sentiment analysis and topic modeling can be implemented using Python and deep learning. Contextual embeddings can help you to identify similar sentences in large datasets and LSTMs can be used for automatic word suggestions. The authors state that transfer learning is effective for building chatbots while recurrent neural networks are useful for document summarization.
Download PDF
5. Deep Learning for NLP and Speech Recognition
2019 by Uday Kamath, John Liu, James Whitaker
This book contains hands-on case studies that demonstrate the implementation of deep learning in speech recognition. It proves that deep learning models (that are used for better understanding of spoken words) are essential for speech recognition and machine translation. NLP-based speech recognition can be integrated into various industries like finance and healthcare. Practical applications of deep learning in NLP include document classification and voice analysis.
Download PDF
How to download PDF:
1. Install Google Books Downloader
2. Enter Book ID to the search box and press Enter
3. Click "Download Book" icon and select PDF*
* - note that for yellow books only preview pages are downloaded
