The rapid advancements in Artificial Intelligence (AI), specifically in Natural Language Processing (NLP) and Generative AI, pose a challenge for academic scholars. Staying current with the latest techniques and applications in these fields is difficult due to their dynamic nature, while the lack of comprehensive resources hinders scholars' ability to effectively utilize these technologies. Advanced Applications of Generative AI and Natural Language Processing Models offers an effective solution to address these challenges. This comprehensive book delves into cutting-edge developments in NLP and Generative AI. It provides insights into the functioning of these technologies, their benefits, and associated challenges. Targeting students, researchers, and professionals in AI, NLP, and computer science, this book serves as a vital reference for deepening knowledge of advanced NLP techniques and staying updated on the latest advancements in generative AI. By providing real-world examples and practical applications, scholars can apply their learnings to solve complex problems across various domains. Embracing Advanced Applications of Generative AI and Natural Language Processing Models equips academic scholars with the necessary knowledge and insights to explore innovative applications and unleash the full potential of generative AI and NLP models for effective problem-solving.

The rapid advancements in Artificial Intelligence (AI), specifically in Natural Language Processing (NLP) and Generative AI, pose a challenge for academic scholars. Staying current with the latest techniques and applications in these fields is difficult due to their dynamic nature, while the lack of comprehensive resources hinders scholars' ability to effectively utilize these technologies. Advanced Applications of Generative AI and Natural Language Processing Models offers an effective solution to address these challenges. This comprehensive book delves into cutting-edge developments in NLP and Generative AI. It provides insights into the functioning of these technologies, their benefits, and associated challenges. Targeting students, researchers, and professionals in AI, NLP, and computer science, this book serves as a vital reference for deepening knowledge of advanced NLP techniques and staying updated on the latest advancements in generative AI. By providing real-world examples and practical applications, scholars can apply their learnings to solve complex problems across various domains. Embracing Advanced Applications of Generative AI and Natural Language Processing Modelsequips academic scholars with the necessary knowledge and insights to explore innovative applications and unleash the full potential of generative AI and NLP models for effective problem-solving.

One-stop solution for NLP practitioners, ML developers, and data scientists to build effective NLP systems that can perform real-world complicated tasks Key FeaturesApply deep learning algorithms and techniques such as BiLSTMS, CRFs, BPE and more using TensorFlow 2Explore applications like text generation, summarization, weakly supervised labelling and moreRead cutting edge material with seminal papers provided in the GitHub repository with full working codeBook Description Recently, there have been tremendous advances in NLP, and we are now moving from research labs into practical applications. This book comes with a perfect blend of both the theoretical and practical aspects of trending and complex NLP techniques. The book is focused on innovative applications in the field of NLP, language generation, and dialogue systems. It helps you apply the concepts of pre-processing text using techniques such as tokenization, parts of speech tagging, and lemmatization using popular libraries such as Stanford NLP and SpaCy. You will build Named Entity Recognition (NER) from scratch using Conditional Random Fields and Viterbi Decoding on top of RNNs. The book covers key emerging areas such as generating text for use in sentence completion and text summarization, bridging images and text by generating captions for images, and managing dialogue aspects of chatbots. You will learn how to apply transfer learning and fine-tuning using TensorFlow 2. Further, it covers practical techniques that can simplify the labelling of textual data. The book also has a working code that is adaptable to your use cases for each tech piece. By the end of the book, you will have an advanced knowledge of the tools, techniques and deep learning architecture used to solve complex NLP problems. What you will learnGrasp important pre-steps in building NLP applications like POS taggingUse transfer and weakly supervised learning using libraries like SnorkelDo sentiment analysis using BERTApply encoder-decoder NN architectures and beam search for summarizing textsUse Transformer models with attention to bring images and text togetherBuild apps that generate captions and answer questions about images using custom TransformersUse advanced TensorFlow techniques like learning rate annealing, custom layers, and custom loss functions to build the latest DeepNLP modelsWho this book is for This is not an introductory book and assumes the reader is familiar with basics of NLP and has fundamental Python skills, as well as basic knowledge of machine learning and undergraduate-level calculus and linear algebra. The readers who can benefit the most from this book include intermediate ML developers who are familiar with the basics of supervised learning and deep learning techniques and professionals who already use TensorFlow/Python for purposes such as data science, ML, research, analysis, etc.

This volume focuses on natural language processing, artificial intelligence, and allied areas. Natural language processing enables communication between people and computers and automatic translation to facilitate easy interaction with others around the world. This book discusses theoretical work and advanced applications, approaches, and techniques for computational models of information and how it is presented by language (artificial, human, or natural) in other ways. It looks at intelligent natural language processing and related models of thought, mental states, reasoning, and other cognitive processes. It explores the difficult problems and challenges related to partiality, underspecification, and context-dependency, which are signature features of information in nature and natural languages. Key features: Addresses the functional frameworks and workflow that are trending in NLP and AI Looks at the latest technologies and the major challenges, issues, and advances in NLP and AI Explores an intelligent field monitoring and automated system through AI with NLP and its implications for the real world Discusses data acquisition and presents a real-time case study with illustrations related to data-intensive technologies in AI and NLP.

Build custom NLP models in record time by adapting pre-trained machine learning models to solve specialized problems. Summary In Transfer Learning for Natural Language Processing you will learn: Fine tuning pretrained models with new domain data Picking the right model to reduce resource usage Transfer learning for neural network architectures Generating text with generative pretrained transformers Cross-lingual transfer learning with BERT Foundations for exploring NLP academic literature Training deep learning NLP models from scratch is costly, time-consuming, and requires massive amounts of data. In Transfer Learning for Natural Language Processing, DARPA researcher Paul Azunre reveals cutting-edge transfer learning techniques that apply customizable pretrained models to your own NLP architectures. You’ll learn how to use transfer learning to deliver state-of-the-art results for language comprehension, even when working with limited label data. Best of all, you’ll save on training time and computational costs. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Build custom NLP models in record time, even with limited datasets! Transfer learning is a machine learning technique for adapting pretrained machine learning models to solve specialized problems. This powerful approach has revolutionized natural language processing, driving improvements in machine translation, business analytics, and natural language generation. About the book Transfer Learning for Natural Language Processing teaches you to create powerful NLP solutions quickly by building on existing pretrained models. This instantly useful book provides crystal-clear explanations of the concepts you need to grok transfer learning along with hands-on examples so you can practice your new skills immediately. As you go, you’ll apply state-of-the-art transfer learning methods to create a spam email classifier, a fact checker, and more real-world applications. What's inside Fine tuning pretrained models with new domain data Picking the right model to reduce resource use Transfer learning for neural network architectures Generating text with pretrained transformers About the reader For machine learning engineers and data scientists with some experience in NLP. About the author Paul Azunre holds a PhD in Computer Science from MIT and has served as a Principal Investigator on several DARPA research programs. Table of Contents PART 1 INTRODUCTION AND OVERVIEW 1 What is transfer learning? 2 Getting started with baselines: Data preprocessing 3 Getting started with baselines: Benchmarking and optimization PART 2 SHALLOW TRANSFER LEARNING AND DEEP TRANSFER LEARNING WITH RECURRENT NEURAL NETWORKS (RNNS) 4 Shallow transfer learning for NLP 5 Preprocessing data for recurrent neural network deep transfer learning experiments 6 Deep transfer learning for NLP with recurrent neural networks PART 3 DEEP TRANSFER LEARNING WITH TRANSFORMERS AND ADAPTATION STRATEGIES 7 Deep transfer learning for NLP with the transformer and GPT 8 Deep transfer learning for NLP with BERT and multilingual BERT 9 ULMFiT and knowledge distillation adaptation strategies 10 ALBERT, adapters, and multitask adaptation strategies 11 Conclusions

In the field of computer vision research, the study of human behavior is a formidable challenge. The diverse applications of this field, from video surveillance for crowd analysis to healthcare diagnostics, have drawn increasing attention. However, a significant problem persists – the sacrifice of transparency for the sake of predictive accuracy in Artificial Intelligence (AI) solutions. These AI systems often operate as enigmatic black boxes, seemingly conjuring decisions from vast datasets with little to no explanation. The need for clarity and accountability in AI decision-making is paramount as our reliance on these systems continues to grow. Explainable AI Applications for Human Behavior Analysis embarks on a mission to harness AI's innate capability to elucidate upon its own decision-making processes. By focusing on facial expressions, gestures, and body movements, we delve into uncharted territories of research, offering novel methodologies, databases, benchmarks, and algorithms for the analysis of human behavior in natural settings. Geared toward academic scholars, this book compiles the expertise of leading researchers in the field, making it accessible to readers of all educational backgrounds.

The rise of Artificial Intelligence (AI) amidst the backdrop of a world that is changing at lightning speed presents a whole new set of challenges. One of our biggest hurdles is more transparency in AI solutions. It's a complex issue, but one that we need to address if we want to ensure that the benefits of AI are accessible to everyone. Across diverse sectors such as healthcare, surveillance, and human-computer interaction, the inability to understand and evaluate AI's decision-making processes hinders progress and raises concerns about accountability. Cross-Industry AI Applications is a groundbreaking solution to illuminate the mysteries of AI-driven human behavior analysis. This pioneering book addresses the necessity of transparency and explainability in AI systems, particularly in analyzing human behavior. Compiling insights from leading experts and academics offers a comprehensive exploration of state-of-the-art methodologies, benchmarks, and systems for understanding and predicting human behavior using AI. Each chapter delves into novel approaches and real-world applications, from facial expressions to gesture recognition, providing invaluable knowledge for scholars and professionals alike.

In an era marked by the rapid integration of Artificial Intelligence (AI) into our lives, the discourse surrounding its implications has intensified. The Role of Generative AI in the Communication Classroom is a pioneering book that delves into the multifaceted dimensions of AI, specifically focusing on OpenAI's revolutionary Chat Generative Pre-Trained Transformer (Chat GPT) and its profound influence on the landscape of communication education. This book navigates the intersection of technology, education, and ethics, shedding light on the imperative need for a collaborative approach to shape AI's evolution. AI's potential to reshape industries and human roles is undeniable. Rooted in the intricate workings of AI and its hallmark, Chat GPT, this book meticulously dissects the dynamic relationship between humans and machines. The discourse extends beyond technology and into the realm of education, asserting that the power to mold AI's trajectory cannot rest solely in the hands of developers. While revealing AI's transformative potential in the communication classroom, the book conscientiously explores ethical concerns and biases, fostering a balanced approach to its integration. This book is instrumental to the ongoing discourse on AI's role in education. The call for ethical considerations, inclusivity, and regulation serves as a guiding compass for educators, students, developers, and policymakers alike. The book ensures a holistic perspective on AI's integration by addressing privacy, citation, voice ownership, and overall digital ethics.

The numerous developments in wireless communications and artificial intelligence (AI) have recently transformed the Internet of Things (IoT) networks to a level of connectivity and intelligence beyond any prior design. This topology is sharply exemplified in mobile edge computing, smart cities, smart homes, smart grids, and the IoT, among many other intelligent applications. Intelligent networks are founded on integrating caching and multi-agent systems that optimize data storage and the entire device’s learning process. However, a central node through which all agents transmit status messages and reward information is a major drawback of this design pattern. This central node condition instigates more communication overhead, potential data leakage, and the birth of data islands. To reverse this trend, using distributed optimization techniques and methodologies in cache-enabled multi-agent learning environments is increasingly beneficial. Advancing Intelligent Networks Through Distributed Optimization explains the current race for sophisticated and accurate distributed optimization in cache-enabled intelligent IoT networks given the need to make multi-agent learning converge faster and reduce communication overhead. These techniques will require innovative resource allocation strategies stretching from system training to caching, communication, and processing amongst millions of agents. This book combines the key recent research in these races into a single binder that can serve all the interested theoretical and practical scholars. The book focuses broadly on intelligent systems’ optimization trends. It identifies the various applications of advanced distributed optimization from manufacturing to medicine, agriculture and smart cities.

Generative artificial intelligence (GAI) represents a profound leap in technological advancement, empowering machines to create content that closely mimics human creativity in various forms. As this technology continues to evolve and permeate multiple industries, it is essential to address the accompanying ethical considerations that arise from its use. Furthermore, there is a need for transparency in how GAI systems are developed and deployed to ensure that they are used responsibly and that their outputs are reliable and fair. Balancing innovation with ethical practices will be crucial to harnessing the benefits of GAI while mitigating its risks and ensuring its positive contribution to society. Responsible Implementations of Generative AI for Multidisciplinary Use highlights both the immense potential of GAI and the ethical challenges it presents. This book demystifies GAI by breaking down complex concepts into accessible language and offering real-world examples that illustrate the implications of its applications. Covering topics such as chatbots, ethical leadership, and the metaverse, this book is an excellent resource for technology professionals and developers, ethicists, policymakers, academicians, researchers, business leaders and executives, legal experts, students, educators, and more.