1 J E Hopcroft R Motwani And J D Ullman Introduction To Automata Theory Languages And Computations Third Edition Pearson Education 2016
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Mathematical Innovation
Author: Mr. A. Durai Ganesh
language: en
Publisher: QUILL TECH PUBLICATIONS
Release Date: 2025-06-16
Mathematical Innovation is a comprehensive and forward-looking exploration of how mathematics drives progress across science, technology, and modern industry. This book presents a rich collection of contemporary theories, applied methodologies, and creative problem-solving approaches that showcase the evolving role of mathematics in solving real-world challenges. Covering both pure and applied mathematics, it bridges classical concepts with emerging fields such as artificial intelligence, data science, optimization, and complex systems. Designed for students, educators, researchers, and professionals, the book highlights interdisciplinary connections and demonstrates how mathematical thinking fuels innovation across diverse domains. Through engaging explanations, illustrative examples, and real-world applications, Mathematical Innovation invites readers to see mathematics not just as a subject, but as a dynamic, essential tool for understanding and shaping the future.
Proceedings of First International Conference on Smart System, Innovations and Computing
The edited volume contains original papers contributed to 1st International Conference on Smart System, Innovations and Computing (SSIC 2017) by researchers from different countries. The contributions focuses on two main areas, i.e. Smart Systems Innovations which includes applications for smart cities, smart grid, social computing and privacy challenges with their theory, specification, design, performance, and system building. And second Computing of Complex Solutions which includes algorithms, security solutions, communication and networking approaches. The volume provides a snapshot of current progress in related areas and a glimpse of future possibilities. This volume is useful for researchers, Ph.D. students, and professionals working in the core areas of smart systems, innovations and computing.
Python for Natural Language Processing
Since the last edition of this book (2014), progress has been astonishing in all areas of Natural Language Processing, with recent achievements in Text Generation that spurred a media interest going beyond the traditional academic circles. Text Processing has meanwhile become a mainstream industrial tool that is used, to various extents, by countless companies. As such, a revision of this book was deemed necessary to catch up with the recent breakthroughs, and the author discusses models and architectures that have been instrumental in the recent progress of Natural Language Processing. As in the first two editions, the intention is to expose the reader to the theories used in Natural Language Processing, and to programming examples that are essential for a deep understanding of the concepts. Although present in the previous two editions, Machine Learning is now even more pregnant, having replaced many of the earlier techniques to process text. Many new techniques build on the availability of text. Using Python notebooks, the reader will be able to load small corpora, format text, apply the models through executing pieces of code, gradually discover the theoretical parts by possibly modifying the code or the parameters, and traverse theories and concrete problems through a constant interaction between the user and the machine. The data sizes and hardware requirements are kept to a reasonable minimum so that a user can see instantly, or at least quickly, the results of most experiments on most machines. The book does not assume a deep knowledge of Python, and an introduction to this language aimed at Text Processing is given in Ch. 2, which will enable the reader to touch all the programming concepts, including NumPy arrays and PyTorch tensors as fundamental structures to represent and process numerical data in Python, or Keras for training Neural Networks to classify texts. Covering topics like Word Segmentation and Part-of-Speech and Sequence Annotation, the textbook also gives an in-depth overview of Transformers (for instance, BERT), Self-Attention and Sequence-to-Sequence Architectures.