Nltk Tutorial
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Deep Learning for Natural Language Processing
Author: Jason Brownlee
language: en
Publisher: Machine Learning Mastery
Release Date: 2017-11-21
Deep learning methods are achieving state-of-the-art results on challenging machine learning problems such as describing photos and translating text from one language to another. In this new laser-focused Ebook, finally cut through the math, research papers and patchwork descriptions about natural language processing. Using clear explanations, standard Python libraries and step-by-step tutorial lessons you will discover what natural language processing is, the promise of deep learning in the field, how to clean and prepare text data for modeling, and how to develop deep learning models for your own natural language processing projects.
Handbook of Language Analysis in Psychology
Author: Morteza Dehghani
language: en
Publisher: Guilford Publications
Release Date: 2022-03-02
Recent years have seen an explosion of interest in the use of computerized text analysis methods to address basic psychological questions. This comprehensive handbook brings together leading language analysis scholars to present foundational concepts and methods for investigating human thought, feeling, and behavior using language. Contributors work toward integrating psychological science and theory with natural language processing (NLP) and machine learning. Ethical issues in working with natural language data sets are discussed in depth. The volume showcases NLP-driven techniques and applications in areas including interpersonal relationships, personality, morality, deception, social biases, political psychology, psychopathology, and public health.
Applied Machine Learning Explainability Techniques
Author: Aditya Bhattacharya
language: en
Publisher: Packt Publishing Ltd
Release Date: 2022-07-29
Leverage top XAI frameworks to explain your machine learning models with ease and discover best practices and guidelines to build scalable explainable ML systems Key Features • Explore various explainability methods for designing robust and scalable explainable ML systems • Use XAI frameworks such as LIME and SHAP to make ML models explainable to solve practical problems • Design user-centric explainable ML systems using guidelines provided for industrial applications Book Description Explainable AI (XAI) is an emerging field that brings artificial intelligence (AI) closer to non-technical end users. XAI makes machine learning (ML) models transparent and trustworthy along with promoting AI adoption for industrial and research use cases. Applied Machine Learning Explainability Techniques comes with a unique blend of industrial and academic research perspectives to help you acquire practical XAI skills. You'll begin by gaining a conceptual understanding of XAI and why it's so important in AI. Next, you'll get the practical experience needed to utilize XAI in AI/ML problem-solving processes using state-of-the-art methods and frameworks. Finally, you'll get the essential guidelines needed to take your XAI journey to the next level and bridge the existing gaps between AI and end users. By the end of this ML book, you'll be equipped with best practices in the AI/ML life cycle and will be able to implement XAI methods and approaches using Python to solve industrial problems, successfully addressing key pain points encountered. What you will learn • Explore various explanation methods and their evaluation criteria • Learn model explanation methods for structured and unstructured data • Apply data-centric XAI for practical problem-solving • Hands-on exposure to LIME, SHAP, TCAV, DALEX, ALIBI, DiCE, and others • Discover industrial best practices for explainable ML systems • Use user-centric XAI to bring AI closer to non-technical end users • Address open challenges in XAI using the recommended guidelines Who this book is for This book is for scientists, researchers, engineers, architects, and managers who are actively engaged in machine learning and related fields. Anyone who is interested in problem-solving using AI will benefit from this book. Foundational knowledge of Python, ML, DL, and data science is recommended. AI/ML experts working with data science, ML, DL, and AI will be able to put their knowledge to work with this practical guide. This book is ideal for you if you're a data and AI scientist, AI/ML engineer, AI/ML product manager, AI product owner, AI/ML researcher, and UX and HCI researcher.