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This book covers language modeling and automatic speech recognition for inflective languages (e.g. Slavic languages), which represent roughly half of the languages spoken in Europe. These languages do not perform as well as English in speech recognition systems and it is therefore harder to develop an application with sufficient quality for the end user. The authors describe the most important language features for the development of a speech recognition system. This is then presented through the analysis of errors in the system and the development of language models and their inclusion in speech recognition systems, which specifically address the errors that are relevant for targeted applications. The error analysis is done with regard to morphological characteristics of the word in the recognized sentences. The book is oriented towards speech recognition with large vocabularies and continuous and even spontaneous speech. Today such applications work with a rather small number of languages compared to the number of spoken languages.

A statisticallanguage model, or more simply a language model, is a prob­ abilistic mechanism for generating text. Such adefinition is general enough to include an endless variety of schemes. However, a distinction should be made between generative models, which can in principle be used to synthesize artificial text, and discriminative techniques to classify text into predefined cat­ egories. The first statisticallanguage modeler was Claude Shannon. In exploring the application of his newly founded theory of information to human language, Shannon considered language as a statistical source, and measured how weH simple n-gram models predicted or, equivalently, compressed natural text. To do this, he estimated the entropy of English through experiments with human subjects, and also estimated the cross-entropy of the n-gram models on natural 1 text. The ability of language models to be quantitatively evaluated in tbis way is one of their important virtues. Of course, estimating the true entropy of language is an elusive goal, aiming at many moving targets, since language is so varied and evolves so quickly. Yet fifty years after Shannon's study, language models remain, by all measures, far from the Shannon entropy liInit in terms of their predictive power. However, tbis has not kept them from being useful for a variety of text processing tasks, and moreover can be viewed as encouragement that there is still great room for improvement in statisticallanguage modeling.

Thepastyearhasbeenaneventfuloneforthoseinterestedinsoftwaremodeling. The?rst major revision of the Uni?ed Modeling Language, UML2.0, is in the process of adoption by the Object Management Group (OMG), and it makes many long-desired additions and improvements to UML. At the same time, it expands what was already a large language. A challenge for both practitioners andresearchersistohelpsmooththeadoptionofthisnewlanguage.Increasingly, attention is being paid to the use of specialized languages, often pro?les of UML, appropriate for di?erent purposes; this is one way to make UML less overwh- ming. Accordingly, the focus of the UML conference is gradually expanding from UML to software modeling in general. Simultaneously, model-driven development is being pursued as a way of - creasing the bene?ts from modeling throughout the software development p- cess. Gradually, it is developing from a set of slogans into a reality. Many of the papers in this volume are concerned, directly or indirectly, with how to make modeling, rather than coding, the heart of software development, and how to realize the resulting bene?ts of higher-level thinking. Much work remains to be done.

This book constitutes the refereed proceedings of the 4th International Conference on the Unified Modeling Language, 2001, held in Toronto, Canada, in October 2001.The 33 revised full papers presented together with one invited paper were carefully reviewed and selected from a total of 122 abstracts and 102 papers submitted. The papers are organized in topical sections on metamodeling, activity diagrams, OCL, architecture and patterns, analysis and testing, performance and databases, graph transformations, real-time and embedded systems, associations and ontology, statecharts, components, and use cases.

Key FeaturesTrain and deploy Recurrent Neural Networks using the popular TensorFlow libraryApply long short-term memory unitsExpand your skills in complex neural network and deep learning topicsBook DescriptionDevelopers struggle to find an easy-to-follow learning resource for implementing Recurrent Neural Network (RNN) models. RNNs are the state-of-the-art model in deep learning for dealing with sequential data. From language translation to generating captions for an image, RNNs are used to continuously improve results. This book will teach you the fundamentals of RNNs, with example applications in Python and the TensorFlow library. The examples are accompanied by the right combination of theoretical knowledge and real-world implementations of concepts to build a solid foundation of neural network modeling. Your journey starts with the simplest RNN model, where you can grasp the fundamentals. The book then builds on this by proposing more advanced and complex algorithms. We use them to explain how a typical state-of-the-art RNN model works. From generating text to building a language translator, we show how some of today's most powerful AI applications work under the hood. After reading the book, you will be confident with the fundamentals of RNNs, and be ready to pursue further study, along with developing skills in this exciting field. What you will learnUse TensorFlow to build RNN modelsUse the correct RNN architecture for a particular machine learning taskCollect and clear the training data for your modelsUse the correct Python libraries for any task during the building phase of your modelOptimize your model for higher accuracyIdentify the differences between multiple models and how you can substitute themLearn the core deep learning fundamentals applicable to any machine learning modelWho this book is forThis book is for Machine Learning engineers and data scientists who want to learn about Recurrent Neural Network models with practical use-cases. Exposure to Python programming is required. Previous experience with TensorFlow will be helpful, but not mandatory.

This book constitutes the refereed proceedings of the 7th International Conference on the Unified Modeling Language, UML 2004, held in Lisbon, Portugal, in October 2004. The 30 revised full papers presented together with summaries on the workshops and tutorials were carefully reviewed and selected from 135 technical paper submissions. The papers are organized in topical sections on metamodeling, aspects, profiles and extensions, OCL, model transformation, verification and model consistency, security, and methodology.

This book presents a theory of consciousness which is unique and sustainable in nature, based on physiological and cognitive-linguistic principles controlled by a number of socio-psycho-economic factors. In order to anchor this theory, which draws upon various disciplines, the author presents a number of different theories, all of which have been abundantly studied by scientists from both a theoretical and experimental standpoint, including models of social organization, ego theories, theories of the motivational system in psychology, theories of the motivational system in neurosciences, language modeling and computational modeling of motivation. The theory presented in this book is based on the hypothesis that an individual's main activities are developed by self-motivation, managed as an informational need. This is described in chapters covering self-motivation on a day-to-day basis, the notion of need, the hypothesis and control of cognitive self-motivation and a model of self-motivation which associates language and physiology. The subject of knowledge extraction is also covered, including the impact of self-motivation on written information, non-transversal and transversal text-mining techniques and the fields of interest of text mining. Contents: 1. Consciousness: an Ancient and Current Topic of Study. 2. Self-motivation on a Daily Basis. 3. The Notion of Need. 4. The Models of Social Organization. 5. Self Theories. 6. Theories of Motivation in Psychology. 7. Theories of Motivation in Neurosciences. 8. Language Modeling. 9. Computational Modeling of Motivation. 10. Hypothesis and Control of Cognitive Self-Motivation. 11. A Model of Self-Motivation which Associates Language and Physiology. 12. Impact of Self-Motivation on Written Information. 13. Non-Transversal Text Mining Techniques. 14. Transversal Text Mining Techniques. 15. Fields of Interest for Text Mining. About the Authors Nicolas Turenne is a researcher at INRA in the Science and Society team at the University of Paris-Est Marne la Vallée in France. He specializes in knowledge extraction from texts with theoretical research into relational and stochastic models. His research topics also concern the sociology of uses, food and environmental sciences, and bioinformatics.

Speech and language technologies continue to grow in importance as they are used to create natural and efficient interfaces between people and machines, and to automatically transcribe, extract, analyze, and route information from high-volume streams of spoken and written information. The workshops on Mathematical Foundations of Speech Processing and Natural Language Modeling were held in the Fall of 2000 at the University of Minnesota's NSF-sponsored Institute for Mathematics and Its Applications, as part of a'Mathematics in Multimedia'year-long program. Each workshop brought together researchers in the respective technologies on the one hand, and mathematicians and statisticians on the other hand, for an intensive week of cross-fertilization. There is a long history of benefit from introducing mathematical techniques and ideas to speech and language technologies. Examples include the source-channel paradigm, hidden Markov models, decision trees, exponential models and formal languages theory. It is likely that new mathematical techniques, or novel applications of existing techniques, will once again prove pivotal for moving the field forward. This volume consists of original contributions presented by participants during the two workshops. Topics include language modeling, prosody, acoustic-phonetic modeling, and statistical methodology.

The ten-volume set LNCS 15016-15025 constitutes the refereed proceedings of the 33rd International Conference on Artificial Neural Networks and Machine Learning, ICANN 2024, held in Lugano, Switzerland, during September 17–20, 2024. The 294 full papers and 16 short papers included in these proceedings were carefully reviewed and selected from 764 submissions. The papers cover the following topics: Part I - theory of neural networks and machine learning; novel methods in machine learning; novel neural architectures; neural architecture search; self-organization; neural processes; novel architectures for computer vision; and fairness in machine learning. Part II - computer vision: classification; computer vision: object detection; computer vision: security and adversarial attacks; computer vision: image enhancement; and computer vision: 3D methods. Part III - computer vision: anomaly detection; computer vision: segmentation; computer vision: pose estimation and tracking; computer vision: video processing; computer vision: generative methods; and topics in computer vision. Part IV - brain-inspired computing; cognitive and computational neuroscience; explainable artificial intelligence; robotics; and reinforcement learning. Part V - graph neural networks; and large language models. Part VI - multimodality; federated learning; and time series processing. Part VII - speech processing; natural language processing; and language modeling. Part VIII - biosignal processing in medicine and physiology; and medical image processing. Part IX - human-computer interfaces; recommender systems; environment and climate; city planning; machine learning in engineering and industry; applications in finance; artificial intelligence in education; social network analysis; artificial intelligence and music; and software security. Part X - workshop: AI in drug discovery; workshop: reservoir computing; special session: accuracy, stability, and robustness in deep neural networks; special session: neurorobotics; and special session: spiking neural networks.

The ten-volume set LNCS 15016-15025 constitutes the refereed proceedings of the 33rd International Conference on Artificial Neural Networks and Machine Learning, ICANN 2024, held in Lugano, Switzerland, during September 17–20, 2024. The 294 full papers and 16 short papers included in these proceedings were carefully reviewed and selected from 764 submissions. The papers cover the following topics: Part I - theory of neural networks and machine learning; novel methods in machine learning; novel neural architectures; neural architecture search; self-organization; neural processes; novel architectures for computer vision; and fairness in machine learning. Part II - computer vision: classification; computer vision: object detection; computer vision: security and adversarial attacks; computer vision: image enhancement; and computer vision: 3D methods. Part III - computer vision: anomaly detection; computer vision: segmentation; computer vision: pose estimation and tracking; computer vision: video processing; computer vision: generative methods; and topics in computer vision. Part IV - brain-inspired computing; cognitive and computational neuroscience; explainable artificial intelligence; robotics; and reinforcement learning. Part V - graph neural networks; and large language models. Part VI - multimodality; federated learning; and time series processing. Part VII - speech processing; natural language processing; and language modeling. Part VIII - biosignal processing in medicine and physiology; and medical image processing. Part IX - human-computer interfaces; recommender systems; environment and climate; city planning; machine learning in engineering and industry; applications in finance; artificial intelligence in education; social network analysis; artificial intelligence and music; and software security. Part X - workshop: AI in drug discovery; workshop: reservoir computing; special session: accuracy, stability, and robustness in deep neural networks; special session: neurorobotics; and special session: spiking neural networks.

The ten-volume set LNCS 15016-15025 constitutes the refereed proceedings of the 33rd International Conference on Artificial Neural Networks and Machine Learning, ICANN 2024, held in Lugano, Switzerland, during September 17–20, 2024. The 294 full papers and 16 short papers included in these proceedings were carefully reviewed and selected from 764 submissions. The papers cover the following topics: Part I - theory of neural networks and machine learning; novel methods in machine learning; novel neural architectures; neural architecture search; self-organization; neural processes; novel architectures for computer vision; and fairness in machine learning. Part II - computer vision: classification; computer vision: object detection; computer vision: security and adversarial attacks; computer vision: image enhancement; and computer vision: 3D methods. Part III - computer vision: anomaly detection; computer vision: segmentation; computer vision: pose estimation and tracking; computer vision: video processing; computer vision: generative methods; and topics in computer vision. Part IV - brain-inspired computing; cognitive and computational neuroscience; explainable artificial intelligence; robotics; and reinforcement learning. Part V - graph neural networks; and large language models. Part VI - multimodality; federated learning; and time series processing. Part VII - speech processing; natural language processing; and language modeling. Part VIII - biosignal processing in medicine and physiology; and medical image processing. Part IX - human-computer interfaces; recommender systems; environment and climate; city planning; machine learning in engineering and industry; applications in finance; artificial intelligence in education; social network analysis; artificial intelligence and music; and software security. Part X - workshop: AI in drug discovery; workshop: reservoir computing; special session: accuracy, stability, and robustness in deep neural networks; special session: neurorobotics; and special session: spiking neural networks.

This book proposes a novel connectionist approach to a challenging topic of language modeling within the context of kernel memory and artificial mind system, both proposed previously by the author in the very first volume of the series, Artificial Mind System—Kernel Memory Approach: Studies in Computational Intelligence, Vol. 1. The present volume focuses on how syntactic structures of language are modeled in terms of the respective composite connectionist architectures, each embracing both the nonsymbolic and symbolic parts. These two parts are developed via inter-module processes within the artificial mind system and eventually integrated under a unified framework of kernel memory. The data representation by the networks embodied within the kernel memory principle is essentially local, unlike conventional artificial neural network models such as the pervasive multilayer perceptron-based neural networks. With this locality principle, kernel memory inherently bears many attractive features, such as topologically unconstrained network formation, straightforward network growing, shrinking, and reconfiguration, no requirement of arduous iterative parameter tuning, construction of transparent and hierarchical data structures, and multimodal and temporal data processing via the network representation. Exploiting these multifacet properties of kernel memory with interweaving the notion of inter-module processing within the artificial mind system provides coherent accounts for concept formation and how various linguistic phenomena, viz. word compoundings, morphologies, and multiword constructions, are modeled. The description is then extended to more intricate network models of context-dependent lexical network and syntactic-oriented processing, the latter being the central theme of the present study, and further to those representing a hybrid of nonverbal and verbal thinking, and semantic and pragmatic aspects of sentential meaning. The book is intended for general readers engaging in various areas of study in cognitive science, computer science, engineering, linguistics, philosophy, psycholinguistics, and psychology.

Get hands-on with GPT 3.5, GPT 4, LangChain, Llama 2, Falcon LLM and more, to build LLM-powered sophisticated AI applicationsKey FeaturesEmbed LLMs into real-world applicationsUse LangChain to orchestrate LLMs and their components within applicationsGrasp basic and advanced techniques of prompt engineeringBook DescriptionBuilding LLM Powered Applications delves into the fundamental concepts, cutting-edge technologies, and practical applications that LLMs offer, ultimately paving the way for the emergence of large foundation models (LFMs) that extend the boundaries of AI capabilities. The book begins with an in-depth introduction to LLMs. We then explore various mainstream architectural frameworks, including both proprietary models (GPT 3.5/4) and open-source models (Falcon LLM), and analyze their unique strengths and differences. Moving ahead, with a focus on the Python-based, lightweight framework called LangChain, we guide you through the process of creating intelligent agents capable of retrieving information from unstructured data and engaging with structured data using LLMs and powerful toolkits. Furthermore, the book ventures into the realm of LFMs, which transcend language modeling to encompass various AI tasks and modalities, such as vision and audio. Whether you are a seasoned AI expert or a newcomer to the field, this book is your roadmap to unlock the full potential of LLMs and forge a new era of intelligent machines.What you will learnExplore the core components of LLM architecture, including encoder-decoder blocks and embeddingsUnderstand the unique features of LLMs like GPT-3.5/4, Llama 2, and Falcon LLMUse AI orchestrators like LangChain, with Streamlit for the frontendGet familiar with LLM components such as memory, prompts, and toolsLearn how to use non-parametric knowledge and vector databasesUnderstand the implications of LFMs for AI research and industry applicationsCustomize your LLMs with fine tuningLearn about the ethical implications of LLM-powered applicationsWho this book is for Software engineers and data scientists who want hands-on guidance for applying LLMs to build applications. The book will also appeal to technical leaders, students, and researchers interested in applied LLM topics. We don't assume previous experience with LLM specifically. But readers should have core ML/software engineering fundamentals to understand and apply the content.

The rapid advancement of technology in Artificial Intelligence (AI), specifically in Natural Language Processing (NLP) and generative AI, presents a challenge for academic scholars to remain informed with the most current information and with the latest techniques and applications. The lack of comprehensive resources also hinders researchers'ability to gain a deep understanding of these subjects and effectively implement these technologies. Advanced Applications of Generative AI and Natural Language Processing Models is a comprehensive resource which offers an effective solution by investigating cutting-edge developments in NLP and generative AI. It provides insights into how these technologies function, their benefits, and the associated challenges. This book serves as a vital reference for deepening knowledge of advanced NLP techniques and staying updated on the latest advancements in generative AI for students, researchers, and professionals in AI, NLP, and computer science. It uses real-world examples and practical applications to empower scholars to apply their knowledge and solve complex problems. Advanced Applications of Generative AI and Natural Language Processing Models is a complete guide that equips researchers with the knowledge needed to utilize NLP and generative AI, including deep learning for NLP, generative models for text generation, language modeling and sentiment analysis, machine translation and multilingual models, ethical considerations in NLP, conversational AI applications, and case studies and real-world applications.

The ten-volume set LNCS 15016-15025 constitutes the refereed proceedings of the 33rd International Conference on Artificial Neural Networks and Machine Learning, ICANN 2024, held in Lugano, Switzerland, during September 17–20, 2024. The 294 full papers and 16 short papers included in these proceedings were carefully reviewed and selected from 764 submissions. The papers cover the following topics: Part I - theory of neural networks and machine learning; novel methods in machine learning; novel neural architectures; neural architecture search; self-organization; neural processes; novel architectures for computer vision; and fairness in machine learning. Part II - computer vision: classification; computer vision: object detection; computer vision: security and adversarial attacks; computer vision: image enhancement; and computer vision: 3D methods. Part III - computer vision: anomaly detection; computer vision: segmentation; computer vision: pose estimation and tracking; computer vision: video processing; computer vision: generative methods; and topics in computer vision. Part IV - brain-inspired computing; cognitive and computational neuroscience; explainable artificial intelligence; robotics; and reinforcement learning. Part V - graph neural networks; and large language models. Part VI - multimodality; federated learning; and time series processing. Part VII - speech processing; natural language processing; and language modeling. Part VIII - biosignal processing in medicine and physiology; and medical image processing. Part IX - human-computer interfaces; recommender systems; environment and climate; city planning; machine learning in engineering and industry; applications in finance; artificial intelligence in education; social network analysis; artificial intelligence and music; and software security. Part X - workshop: AI in drug discovery; workshop: reservoir computing; special session: accuracy, stability, and robustness in deep neural networks; special session: neurorobotics; and special session: spiking neural networks.

The ten-volume set LNCS 15016-15025 constitutes the refereed proceedings of the 33rd International Conference on Artificial Neural Networks and Machine Learning, ICANN 2024, held in Lugano, Switzerland, during September 17–20, 2024. The 294 full papers and 16 short papers included in these proceedings were carefully reviewed and selected from 764 submissions. The papers cover the following topics: Part I - theory of neural networks and machine learning; novel methods in machine learning; novel neural architectures; neural architecture search; self-organization; neural processes; novel architectures for computer vision; and fairness in machine learning. Part II - computer vision: classification; computer vision: object detection; computer vision: security and adversarial attacks; computer vision: image enhancement; and computer vision: 3D methods. Part III - computer vision: anomaly detection; computer vision: segmentation; computer vision: pose estimation and tracking; computer vision: video processing; computer vision: generative methods; and topics in computer vision. Part IV - brain-inspired computing; cognitive and computational neuroscience; explainable artificial intelligence; robotics; and reinforcement learning. Part V - graph neural networks; and large language models. Part VI - multimodality; federated learning; and time series processing. Part VII - speech processing; natural language processing; and language modeling. Part VIII - biosignal processing in medicine and physiology; and medical image processing. Part IX - human-computer interfaces; recommender systems; environment and climate; city planning; machine learning in engineering and industry; applications in finance; artificial intelligence in education; social network analysis; artificial intelligence and music; and software security. Part X - workshop: AI in drug discovery; workshop: reservoir computing; special session: accuracy, stability, and robustness in deep neural networks; special session: neurorobotics; and special session: spiking neural networks.

The ten-volume set LNCS 15016-15025 constitutes the refereed proceedings of the 33rd International Conference on Artificial Neural Networks and Machine Learning, ICANN 2024, held in Lugano, Switzerland, during September 17–20, 2024. The 294 full papers and 16 short papers included in these proceedings were carefully reviewed and selected from 764 submissions. The papers cover the following topics: Part I - theory of neural networks and machine learning; novel methods in machine learning; novel neural architectures; neural architecture search; self-organization; neural processes; novel architectures for computer vision; and fairness in machine learning. Part II - computer vision: classification; computer vision: object detection; computer vision: security and adversarial attacks; computer vision: image enhancement; and computer vision: 3D methods. Part III - computer vision: anomaly detection; computer vision: segmentation; computer vision: pose estimation and tracking; computer vision: video processing; computer vision: generative methods; and topics in computer vision. Part IV - brain-inspired computing; cognitive and computational neuroscience; explainable artificial intelligence; robotics; and reinforcement learning. Part V - graph neural networks; and large language models. Part VI - multimodality; federated learning; and time series processing. Part VII - speech processing; natural language processing; and language modeling. Part VIII - biosignal processing in medicine and physiology; and medical image processing. Part IX - human-computer interfaces; recommender systems; environment and climate; city planning; machine learning in engineering and industry; applications in finance; artificial intelligence in education; social network analysis; artificial intelligence and music; and software security. Part X - workshop: AI in drug discovery; workshop: reservoir computing; special session: accuracy, stability, and robustness in deep neural networks; special session: neurorobotics; and special session: spiking neural networks.

The ten-volume set LNCS 15016-15025 constitutes the refereed proceedings of the 33rd International Conference on Artificial Neural Networks and Machine Learning, ICANN 2024, held in Lugano, Switzerland, during September 17–20, 2024. The 294 full papers and 16 short papers included in these proceedings were carefully reviewed and selected from 764 submissions. The papers cover the following topics: Part I - theory of neural networks and machine learning; novel methods in machine learning; novel neural architectures; neural architecture search; self-organization; neural processes; novel architectures for computer vision; and fairness in machine learning. Part II - computer vision: classification; computer vision: object detection; computer vision: security and adversarial attacks; computer vision: image enhancement; and computer vision: 3D methods. Part III - computer vision: anomaly detection; computer vision: segmentation; computer vision: pose estimation and tracking; computer vision: video processing; computer vision: generative methods; and topics in computer vision. Part IV - brain-inspired computing; cognitive and computational neuroscience; explainable artificial intelligence; robotics; and reinforcement learning. Part V - graph neural networks; and large language models. Part VI - multimodality; federated learning; and time series processing. Part VII - speech processing; natural language processing; and language modeling. Part VIII - biosignal processing in medicine and physiology; and medical image processing. Part IX - human-computer interfaces; recommender systems; environment and climate; city planning; machine learning in engineering and industry; applications in finance; artificial intelligence in education; social network analysis; artificial intelligence and music; and software security. Part X - workshop: AI in drug discovery; workshop: reservoir computing; special session: accuracy, stability, and robustness in deep neural networks; special session: neurorobotics; and special session: spiking neural networks.

The ten-volume set LNCS 15016-15025 constitutes the refereed proceedings of the 33rd International Conference on Artificial Neural Networks and Machine Learning, ICANN 2024, held in Lugano, Switzerland, during September 17–20, 2024. The 294 full papers and 16 short papers included in these proceedings were carefully reviewed and selected from 764 submissions. The papers cover the following topics: Part I - theory of neural networks and machine learning; novel methods in machine learning; novel neural architectures; neural architecture search; self-organization; neural processes; novel architectures for computer vision; and fairness in machine learning. Part II - computer vision: classification; computer vision: object detection; computer vision: security and adversarial attacks; computer vision: image enhancement; and computer vision: 3D methods. Part III - computer vision: anomaly detection; computer vision: segmentation; computer vision: pose estimation and tracking; computer vision: video processing; computer vision: generative methods; and topics in computer vision. Part IV - brain-inspired computing; cognitive and computational neuroscience; explainable artificial intelligence; robotics; and reinforcement learning. Part V - graph neural networks; and large language models. Part VI - multimodality; federated learning; and time series processing. Part VII - speech processing; natural language processing; and language modeling. Part VIII - biosignal processing in medicine and physiology; and medical image processing. Part IX - human-computer interfaces; recommender systems; environment and climate; city planning; machine learning in engineering and industry; applications in finance; artificial intelligence in education; social network analysis; artificial intelligence and music; and software security. Part X - workshop: AI in drug discovery; workshop: reservoir computing; special session: accuracy, stability, and robustness in deep neural networks; special session: neurorobotics; and special session: spiking neural networks.

The ten-volume set LNCS 15016-15025 constitutes the refereed proceedings of the 33rd International Conference on Artificial Neural Networks and Machine Learning, ICANN 2024, held in Lugano, Switzerland, during September 17–20, 2024. The 294 full papers and 16 short papers included in these proceedings were carefully reviewed and selected from 764 submissions. The papers cover the following topics: Part I - theory of neural networks and machine learning; novel methods in machine learning; novel neural architectures; neural architecture search; self-organization; neural processes; novel architectures for computer vision; and fairness in machine learning. Part II - computer vision: classification; computer vision: object detection; computer vision: security and adversarial attacks; computer vision: image enhancement; and computer vision: 3D methods. Part III - computer vision: anomaly detection; computer vision: segmentation; computer vision: pose estimation and tracking; computer vision: video processing; computer vision: generative methods; and topics in computer vision. Part IV - brain-inspired computing; cognitive and computational neuroscience; explainable artificial intelligence; robotics; and reinforcement learning. Part V - graph neural networks; and large language models. Part VI - multimodality; federated learning; and time series processing. Part VII - speech processing; natural language processing; and language modeling. Part VIII - biosignal processing in medicine and physiology; and medical image processing. Part IX - human-computer interfaces; recommender systems; environment and climate; city planning; machine learning in engineering and industry; applications in finance; artificial intelligence in education; social network analysis; artificial intelligence and music; and software security. Part X - workshop: AI in drug discovery; workshop: reservoir computing; special session: accuracy, stability, and robustness in deep neural networks; special session: neurorobotics; and special session: spiking neural networks.

The ten-volume set LNCS 15016-15025 constitutes the refereed proceedings of the 33rd International Conference on Artificial Neural Networks and Machine Learning, ICANN 2024, held in Lugano, Switzerland, during September 17–20, 2024. The 294 full papers and 16 short papers included in these proceedings were carefully reviewed and selected from 764 submissions. The papers cover the following topics: Part I - theory of neural networks and machine learning; novel methods in machine learning; novel neural architectures; neural architecture search; self-organization; neural processes; novel architectures for computer vision; and fairness in machine learning. Part II - computer vision: classification; computer vision: object detection; computer vision: security and adversarial attacks; computer vision: image enhancement; and computer vision: 3D methods. Part III - computer vision: anomaly detection; computer vision: segmentation; computer vision: pose estimation and tracking; computer vision: video processing; computer vision: generative methods; and topics in computer vision. Part IV - brain-inspired computing; cognitive and computational neuroscience; explainable artificial intelligence; robotics; and reinforcement learning. Part V - graph neural networks; and large language models. Part VI - multimodality; federated learning; and time series processing. Part VII - speech processing; natural language processing; and language modeling. Part VIII - biosignal processing in medicine and physiology; and medical image processing. Part IX - human-computer interfaces; recommender systems; environment and climate; city planning; machine learning in engineering and industry; applications in finance; artificial intelligence in education; social network analysis; artificial intelligence and music; and software security. Part X - workshop: AI in drug discovery; workshop: reservoir computing; special session: accuracy, stability, and robustness in deep neural networks; special session: neurorobotics; and special session: spiking neural networks.

Energy-Based Models (EBMs) are an important class of probabilistic models, also known as random fields and undirected graphical models. EBMs are un-normalized and thus radically different from other popular self-normalized probabilistic models such as hidden Markov models (HMMs), autoregressive models, generative adversarial nets (GANs) and variational auto-encoders (VAEs). Over the past years, EBMs have attracted increasing interest not only from the core machine learning community, but also from application domains such as speech, vision, natural language processing (NLP) and so on, due to significant theoretical and algorithmic progress. The sequential nature of speech and language also presents special challenges and needs a different treatment from processing fix-dimensional data (e.g., images). Therefore, the purpose of this monograph is to present a systematic introduction to energy-based models, including both algorithmic progress and applications in speech and language processing. First, the basics of EBMs are introduced, including classic models, recent models parameterized by neural networks, sampling methods, and various learning methods from the classic learning algorithms to the most advanced ones. Then, the application of EBMs in three different scenarios is presented, i.e., for modeling marginal, conditional and joint distributions, respectively. 1) EBMs for sequential data with applications in language modeling, where the main focus is on the marginal distribution of a sequence itself; 2) EBMs for modeling conditional distributions of target sequences given observation sequences, with applications in speech recognition, sequence labeling and text generation; 3) EBMs for modeling joint distributions of both sequences of observations and targets, and their applications in semi-supervised learning and calibrated natural language understanding. Lastly, several open-source toolkits are introduced to help readers get familiar with the techniques for developing and applying energy-based models.

Systems Biology is an approach to biology that involves understanding the complexity of interactions among biological entities within a systemic whole. The goal is to understand the emergence of physiological or functional properties. Symbolic Approaches to Modeling and Analysis of Biological Systems presents contributions of formal methods from computer science for modeling the dynamics of biological systems. It deals more specifically with symbolic methods, i.e. methods that can establish the qualitative properties of models. This book presents different approaches related to semantics, language, modeling and their link with data, and allows us to examine the fundamental problems and challenges that biological systems are facing. The first part of the book presents works that rely on various available data to build models, while the second part gathers contributions surrounding issues of semantics and formal methods.

The introduction of Quantum Theory (QT) provides a unified mathematical framework for Information Retrieval (IR). Compared with the classical IR framework, the quantum-inspired IR framework is based on user-centered modeling methods to model non-classical cognitive phenomena in human relevance judgment in the IR process. With the increase of data and computing resources, neural IR methods have been applied to the text matching and understanding task of IR. Neural networks have a strong learning ability of effective representation and generalization of matching patterns from raw data. This monograph provides a systematic introduction to quantum-inspired neural IR, including quantum-inspired neural language representation, matching and understanding. The cross-field research on QT, neural network and IR is not only helpful to non-classical phenomena modeling in IR but also to break the theoretical bottleneck of neural networks and design more transparent neural IR models. The authors first introduce the language representation method based on QT. Secondly, they introduce the quantum-inspired text matching and decision making model under neural network that shows its theoretical advantages in document ranking, relevance matching, multimodal IR, and can be integrated with neural network to jointly promote the development of IR. Finally, the latest progress of quantum language understanding is introduced and further topics on QT and language modeling provide readers with more materials for thinking.

The text discusses recurrent neural networks for prediction and offers new insights into the learning algorithms, architectures, and stability of recurrent neural networks. It discusses important topics including recurrent and folding networks, long short-term memory (LSTM) networks, gated recurrent unit neural networks, language modeling, neural network model, activation function, feed-forward network, learning algorithm, neural turning machines, and approximation ability. The text discusses diverse applications in areas including air pollutant modeling and prediction, attractor discovery and chaos, ECG signal processing, and speech processing. Case studies are interspersed throughout the book for better understanding.FEATURES Covers computational analysis and understanding of natural languages Discusses applications of recurrent neural network in e-Healthcare Provides case studies in every chapter with respect to real-world scenarios Examines open issues with natural language, health care, multimedia (Audio/Video), transportation, stock market, and logistics The text is primarily written for undergraduate and graduate students, researchers, and industry professionals in the fields of electrical, electronics and communication, and computer engineering/information technology.

The two-volume set LNCS 13451 and 13452 constitutes revised selected papers from the CICLing 2019 conference which took place in La Rochelle, France, April 2019.The total of 95 papers presented in the two volumes was carefully reviewed and selected from 335 submissions. The book also contains 3 invited papers. The papers are organized in the following topical sections: General, Information extraction, Information retrieval, Language modeling, Lexical resources, Machine translation, Morphology, sintax, parsing, Name entity recognition, Semantics and text similarity, Sentiment analysis, Speech processing, Text categorization, Text generation, and Text mining.

The two-volume set LNCS 13451 and 13452 constitutes revised selected papers from the CICLing 2019 conference which took place in La Rochelle, France, April 2019.The total of 95 papers presented in the two volumes was carefully reviewed and selected from 335 submissions. The book also contains 3 invited papers. The papers are organized in the following topical sections: General, Information extraction, Information retrieval, Language modeling, Lexical resources, Machine translation, Morphology, sintax, parsing, Name entity recognition, Semantics and text similarity, Sentiment analysis, Speech processing, Text categorization, Text generation, and Text mining.

This book will teach you the underlying concepts of large language models (LLMs), as well as the technologies associated with them. The book starts with an introduction to the rise of conversational AIs such as ChatGPT, and how they are related to the broader spectrum of large language models. From there, you will learn about natural language processing (NLP), its core concepts, and how it has led to the rise of LLMs. Next, you will gain insight into transformers and how their characteristics, such as self-attention, enhance the capabilities of language modeling, along with the unique capabilities of LLMs. The book concludes with an exploration of the architectures of various LLMs and the opportunities presented by their ever-increasing capabilities—as well as the dangers of their misuse. After completing this book, you will have a thorough understanding of LLMs and will be ready to take your first steps in implementing them into your own projects. What You Will Learn Grasp the underlying concepts of LLMsGain insight into how the concepts and approaches of NLP have evolved over the yearsUnderstand transformer models and attention mechanismsExplore different types of LLMs and their applicationsUnderstand the architectures of popular LLMsDelve into misconceptions and concerns about LLMs, as well as how to best utilize them Who This Book Is For Anyone interested in learning the foundational concepts of NLP, LLMs, and recent advancements of deep learning

A modern information retrieval system must have the capability to find, organize and present very different manifestations of information – such as text, pictures, videos or database records – any of which may be of relevance to the user. However, the concept of relevance, while seemingly intuitive, is actually hard to define, and it's even harder to model in a formal way. Lavrenko does not attempt to bring forth a new definition of relevance, nor provide arguments as to why any particular definition might be theoretically superior or more complete. Instead, he takes a widely accepted, albeit somewhat conservative definition, makes several assumptions, and from them develops a new probabilistic model that explicitly captures that notion of relevance. With this book, he makes two major contributions to the field of information retrieval: first, a new way to look at topical relevance, complementing the two dominant models, i.e., the classical probabilistic model and the language modeling approach, and which explicitly combines documents, queries, and relevance in a single formalism; second, a new method for modeling exchangeable sequences of discrete random variables which does not make any structural assumptions about the data and which can also handle rare events. Thus his book is of major interest to researchers and graduate students in information retrieval who specialize in relevance modeling, ranking algorithms, and language modeling.

Latent semantic mapping (LSM) is a generalization of latent semantic analysis (LSA), a paradigm originally developed to capture hidden word patterns in a text document corpus. In information retrieval, LSA enables retrieval on the basis of conceptual content, instead of merely matching words between queries and documents. It operates under the assumption that there is some latent semantic structure in the data, which is partially obscured by the randomness of word choice with respect to retrieval. Algebraic and/or statistical techniques are brought to bear to estimate this structure and get rid of the obscuring''noise.''This results in a parsimonious continuous parameter description of words and documents, which then replaces the original parameterization in indexing and retrieval. This approach exhibits three main characteristics: -Discrete entities (words and documents) are mapped onto a continuous vector space; -This mapping is determined by global correlation patterns; and -Dimensionality reduction is an integral part of the process. Such fairly generic properties are advantageous in a variety of different contexts, which motivates a broader interpretation of the underlying paradigm. The outcome (LSM) is a data-driven framework for modeling meaningful global relationships implicit in large volumes of (not necessarily textual) data. This monograph gives a general overview of the framework, and underscores the multifaceted benefits it can bring to a number of problems in natural language understanding and spoken language processing. It concludes with a discussion of the inherent tradeoffs associated with the approach, and some perspectives on its general applicability to data-driven information extraction. Contents: I. Principles / Introduction / Latent Semantic Mapping / LSM Feature Space / Computational Effort / Probabilistic Extensions / II. Applications / Junk E-mail Filtering / Semantic Classification / Language Modeling / Pronunciation Modeling / Speaker Verification / TTS Unit Selection / III. Perspectives / Discussion / Conclusion / Bibliography

Unlock the TensorFlow design secrets behind successful deep learning applications! Deep learning StackOverflow contributor Thushan Ganegedara teaches you the new features of TensorFlow 2 in this hands-on guide.In TensorFlow in Action you will learn: Fundamentals of TensorFlow Implementing deep learning networks Picking a high-level Keras API for model building with confidence Writing comprehensive end-to-end data pipelines Building models for computer vision and natural language processing Utilizing pretrained NLP models Recent algorithms including transformers, attention models, and ElMo In TensorFlow in Action, you'll dig into the newest version of Google's amazing TensorFlow framework as you learn to create incredible deep learning applications. Author Thushan Ganegedara uses quirky stories, practical examples, and behind-the-scenes explanations to demystify concepts otherwise trapped in dense academic papers. As you dive into modern deep learning techniques like transformer and attention models, you'll benefit from the unique insights of a top StackOverflow contributor for deep learning and NLP. About the technology Google's TensorFlow framework sits at the heart of modern deep learning. Boasting practical features like multi-GPU support, network data visualization, and easy production pipelines using TensorFlow Extended (TFX), TensorFlow provides the most efficient path to professional AI applications. And the Keras library, fully integrated into TensorFlow 2, makes it a snap to build and train even complex models for vision, language, and more. About the book TensorFlow in Action teaches you to construct, train, and deploy deep learning models using TensorFlow 2. In this practical tutorial, you'll build reusable skill hands-on as you create production-ready applications such as a French-to-English translator and a neural network that can write fiction. You'll appreciate the in-depth explanations that go from DL basics to advanced applications in NLP, image processing, and MLOps, complete with important details that you'll return to reference over and over. What's inside Covers TensorFlow 2.9 Recent algorithms including transformers, attention models, and ElMo Build on pretrained models Writing end-to-end data pipelines with TFX About the reader For Python programmers with basic deep learning skills. About the author Thushan Ganegedara is a senior ML engineer at Canva and TensorFlow expert. He holds a PhD in machine learning from the University of Sydney. Table of Contents PART 1 FOUNDATIONS OF TENSORFLOW 2 AND DEEP LEARNING 1 The amazing world of TensorFlow 2 TensorFlow 2 3 Keras and data retrieval in TensorFlow 2 4 Dipping toes in deep learning 5 State-of-the-art in deep learning: Transformers PART 2 LOOK MA, NO HANDS! DEEP NETWORKS IN THE REAL WORLD 6 Teaching machines to see: Image classification with CNNs 7 Teaching machines to see better: Improving CNNs and making them confess 8 Telling things apart: Image segmentation 9 Natural language processing with TensorFlow: Sentiment analysis 10 Natural language processing with TensorFlow: Language modeling PART 3 ADVANCED DEEP NETWORKS FOR COMPLEX PROBLEMS 11 Sequence-to-sequence learning: Part 1 12 Sequence-to-sequence learning: Part 2 13 Transformers 14 TensorBoard: Big brother of TensorFlow 15 TFX: MLOps and deploying models with TensorFlow

Take your Python text processing skills to another level by learning about the latest natural language processing and machine learning techniques with this full color guideKey FeaturesLearn how to acquire and process textual data and visualize the key findingsObtain deeper insight into the most commonly used algorithms and techniques and understand their tradeoffsImplement models for solving real-world problems and evaluate their performanceBook DescriptionWith the ever-increasing demand for machine learning and programming professionals, it's prime time to invest in the field. This book will help you in this endeavor, focusing specifically on text data and human language by steering a middle path among the various textbooks that present complicated theoretical concepts or focus disproportionately on Python code. A good metaphor this work builds upon is the relationship between an experienced craftsperson and their trainee. Based on the current problem, the former picks a tool from the toolbox, explains its utility, and puts it into action. This approach will help you to identify at least one practical use for each method or technique presented. The content unfolds in ten chapters, each discussing one specific case study. For this reason, the book is solution-oriented. It's accompanied by Python code in the form of Jupyter notebooks to help you obtain hands-on experience. A recurring pattern in the chapters of this book is helping you get some intuition on the data and then implement and contrast various solutions. By the end of this book, you'll be able to understand and apply various techniques with Python for text preprocessing, text representation, dimensionality reduction, machine learning, language modeling, visualization, and evaluation.What you will learnUnderstand fundamental concepts of machine learning for textDiscover how text data can be represented and build language modelsPerform exploratory data analysis on text corporaUse text preprocessing techniques and understand their trade-offsApply dimensionality reduction for visualization and classificationIncorporate and fine-tune algorithms and models for machine learningEvaluate the performance of the implemented systemsKnow the tools for retrieving text data and visualizing the machine learning workflowWho this book is forThis book is for professionals in the area of computer science, programming, data science, informatics, business analytics, statistics, language technology, and more who aim for a gentle career shift in machine learning for text. Students in relevant disciplines that seek a textbook in the field will benefit from the practical aspects of the content and how the theory is presented. Finally, professors teaching a similar course will be able to pick pertinent topics in terms of content and difficulty. Beginner-level knowledge of Python programming is needed to get started with this book.

Publisher's Note: A new edition of this book is out now that includes working with GPT-3 and comparing the results with other models. It includes even more use cases, such as casual language analysis and computer vision tasks, as well as an introduction to OpenAI's Codex.Key FeaturesBuild and implement state-of-the-art language models, such as the original Transformer, BERT, T5, and GPT-2, using concepts that outperform classical deep learning modelsGo through hands-on applications in Python using Google Colaboratory Notebooks with nothing to install on a local machineTest transformer models on advanced use casesBook DescriptionThe transformer architecture has proved to be revolutionary in outperforming the classical RNN and CNN models in use today. With an apply-as-you-learn approach, Transformers for Natural Language Processing investigates in vast detail the deep learning for machine translations, speech-to-text, text-to-speech, language modeling, question answering, and many more NLP domains with transformers. The book takes you through NLP with Python and examines various eminent models and datasets within the transformer architecture created by pioneers such as Google, Facebook, Microsoft, OpenAI, and Hugging Face. The book trains you in three stages. The first stage introduces you to transformer architectures, starting with the original transformer, before moving on to RoBERTa, BERT, and DistilBERT models. You will discover training methods for smaller transformers that can outperform GPT-3 in some cases. In the second stage, you will apply transformers for Natural Language Understanding (NLU) and Natural Language Generation (NLG). Finally, the third stage will help you grasp advanced language understanding techniques such as optimizing social network datasets and fake news identification. By the end of this NLP book, you will understand transformers from a cognitive science perspective and be proficient in applying pretrained transformer models by tech giants to various datasets.What you will learnUse the latest pretrained transformer modelsGrasp the workings of the original Transformer, GPT-2, BERT, T5, and other transformer modelsCreate language understanding Python programs using concepts that outperform classical deep learning modelsUse a variety of NLP platforms, including Hugging Face, Trax, and AllenNLPApply Python, TensorFlow, and Keras programs to sentiment analysis, text summarization, speech recognition, machine translations, and moreMeasure the productivity of key transformers to define their scope, potential, and limits in productionWho this book is forSince the book does not teach basic programming, you must be familiar with neural networks, Python, PyTorch, and TensorFlow in order to learn their implementation with Transformers. Readers who can benefit the most from this book include experienced deep learning & NLP practitioners and data analysts & data scientists who want to process the increasing amounts of language-driven data.

Real-world Natural Language Processing shows you how to build the practical NLP applications that are transforming the way humans and computers work together.In Real-world Natural Language Processing you will learn how to: Design, develop, and deploy useful NLP applications Create named entity taggers Build machine translation systems Construct language generation systems and chatbots Use advanced NLP concepts such as attention and transfer learning Real-world Natural Language Processing teaches you how to create practical NLP applications without getting bogged down in complex language theory and the mathematics of deep learning. In this engaging book, you'll explore the core tools and techniques required to build a huge range of powerful NLP apps, including chatbots, language detectors, and text classifiers. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Training computers to interpret and generate speech and text is a monumental challenge, and the payoff for reducing labor and improving human/computer interaction is huge! Th e field of Natural Language Processing (NLP) is advancing rapidly, with countless new tools and practices. This unique book offers an innovative collection of NLP techniques with applications in machine translation, voice assistants, text generation, and more. About the book Real-world Natural Language Processing shows you how to build the practical NLP applications that are transforming the way humans and computers work together. Guided by clear explanations of each core NLP topic, you'll create many interesting applications including a sentiment analyzer and a chatbot. Along the way, you'll use Python and open source libraries like AllenNLP and HuggingFace Transformers to speed up your development process. What's inside Design, develop, and deploy useful NLP applications Create named entity taggers Build machine translation systems Construct language generation systems and chatbots About the reader For Python programmers. No prior machine learning knowledge assumed. About the author Masato Hagiwara received his computer science PhD from Nagoya University in 2009. He has interned at Google and Microsoft Research, and worked at Duolingo as a Senior Machine Learning Engineer. He now runs his own research and consulting company. Table of Contents PART 1 BASICS 1 Introduction to natural language processing 2 Your first NLP application 3 Word and document embeddings 4 Sentence classification 5 Sequential labeling and language modeling PART 2 ADVANCED MODELS 6 Sequence-to-sequence models 7 Convolutional neural networks 8 Attention and Transformer 9 Transfer learning with pretrained language models PART 3 PUTTING INTO PRODUCTION 10 Best practices in developing NLP applications 11 Deploying and serving NLP applications

This book offers insights on the study of natural language as a complex adaptive system. It discusses a new way to tackle the problem of language modeling, and provides clues on how the close relation between natural language and some biological structures can be very fruitful for science. The book examines the theoretical framework and then applies its main principles to various areas of linguistics. It discusses applications in language contact, language change, diachronic linguistics, and the potential enhancement of classical approaches to historical linguistics by means of new methodologies used in physics, biology, and agent systems theory. It shows how studying language evolution and change using computational simulations enables to integrate social structures in the evolution of language, and how this can give rise to a new way to approach sociolinguistics. Finally, it explores applications for discourse analysis, semantics and cognition.

This textbook explains Deep Learning Architecture, with applications to various NLP Tasks, including Document Classification, Machine Translation, Language Modeling, and Speech Recognition. With the widespread adoption of deep learning, natural language processing (NLP),and speech applications in many areas (including Finance, Healthcare, and Government) there is a growing need for one comprehensive resource that maps deep learning techniques to NLP and speech and provides insights into using the tools and libraries for real-world applications. Deep Learning for NLP and Speech Recognition explains recent deep learning methods applicable to NLP and speech, provides state-of-the-art approaches, and offers real-world case studies with code to provide hands-on experience. Many books focus on deep learning theory or deep learning for NLP-specific tasks while others are cookbooks for tools and libraries, but the constant flux of new algorithms, tools, frameworks, and libraries in a rapidly evolving landscape means that there are few available texts that offer the material in this book. The book is organized into three parts, aligning to different groups of readers and their expertise. The three parts are: Machine Learning, NLP, and Speech Introduction The first part has three chapters that introduce readers to the fields of NLP, speech recognition, deep learning and machine learning with basic theory and hands-on case studies using Python-based tools and libraries. Deep Learning Basics The five chapters in the second part introduce deep learning and various topics that are crucial for speech and text processing, including word embeddings, convolutional neural networks, recurrent neural networks and speech recognition basics. Theory, practical tips, state-of-the-art methods, experimentations and analysis in using the methods discussed in theory on real-world tasks. Advanced Deep Learning Techniques for Text and Speech The third part has five chapters that discuss the latest and cutting-edge research in the areas of deep learning that intersect with NLP and speech. Topics including attention mechanisms, memory augmented networks, transfer learning, multi-task learning, domain adaptation, reinforcement learning, and end-to-end deep learning for speech recognition are covered using case studies.

Build and train neural network models with high speed and flexibility in text, vision, and advanced analytics using PyTorch 1.xKey FeaturesGain a thorough understanding of the PyTorch framework and learn to implement neural network architecturesUnderstand GPU computing to perform heavy deep learning computations using PythonApply cutting-edge natural language processing (NLP) techniques to solve problems with textual dataBook DescriptionPyTorch is gaining the attention of deep learning researchers and data science professionals due to its accessibility and efficiency, along with the fact that it's more native to the Python way of development. This book will get you up and running with this cutting-edge deep learning library, effectively guiding you through implementing deep learning concepts.In this second edition, you'll learn the fundamental aspects that power modern deep learning, and explore the new features of the PyTorch 1.x library. You'll understand how to solve real-world problems using CNNs, RNNs, and LSTMs, along with discovering state-of-the-art modern deep learning architectures, such as ResNet, DenseNet, and Inception. You'll then focus on applying neural networks to domains such as computer vision and NLP. Later chapters will demonstrate how to build, train, and scale a model with PyTorch and also cover complex neural networks such as GANs and autoencoders for producing text and images. In addition to this, you'll explore GPU computing and how it can be used to perform heavy computations. Finally, you'll learn how to work with deep learning-based architectures for transfer learning and reinforcement learning problems.By the end of this book, you'll be able to confidently and easily implement deep learning applications in PyTorch.What you will learnBuild text classification and language modeling systems using neural networksImplement transfer learning using advanced CNN architecturesUse deep reinforcement learning techniques to solve optimization problems in PyTorchMix multiple models for a powerful ensemble modelBuild image classifiers by implementing CNN architectures using PyTorchGet up to speed with reinforcement learning, GANs, LSTMs, and RNNs with real-world examplesWho this book is forThis book is for data scientists and machine learning engineers looking to work with deep learning algorithms using PyTorch 1.x. You will also find this book useful if you want to migrate to PyTorch 1.x. Working knowledge of Python programming and some understanding of machine learning will be helpful.

This book introduces readers to the fundamental concepts of deep learning and offers practical insights into how this learning paradigm supports automatic mechanisms of structural knowledge representation. It discusses a number of multilayer architectures giving rise to tangible and functionally meaningful pieces of knowledge, and shows how the structural developments have become essential to the successful delivery of competitive practical solutions to real-world problems. The book also demonstrates how the architectural developments, which arise in the setting of deep learning, support detailed learning and refinements to the system design. Featuring detailed descriptions of the current trends in the design and analysis of deep learning topologies, the book offers practical guidelines and presents competitive solutions to various areas of language modeling, graph representation, and forecasting.

Grasp the fundamental concepts of deep learning using Tensorflow in a hands-on mannerKey Features[•] Get a first-hand experience of the deep learning concepts and techniques with this easy-to-follow guide[•] Train different types of neural networks using Tensorflow for real-world problems in language processing, computer vision, transfer learning, and more[•] Designed for those who believe in the concept of'learn by doing', this book is a perfect blend of theory and code examplesBook DescriptionDeep learning is a popular subset of machine learning, and it allows you to build complex models that are faster and give more accurate predictions. This book is your companion to take your first steps into the world of deep learning, with hands-on examples to boost your understanding of the topic. This book starts with a quick overview of the essential concepts of data science and machine learning which are required to get started with deep learning. It introduces you to Tensorflow, the most widely used machine learning library for training deep learning models. You will then work on your first deep learning problem by training a deep feed-forward neural network for digit classification, and move on to tackle other real-world problems in computer vision, language processing, sentiment analysis, and more. Advanced deep learning models such as generative adversarial networks and their applications are also covered in this book. By the end of this book, you will have a solid understanding of all the essential concepts in deep learning. With the help of the examples and code provided in this book, you will be equipped to train your own deep learning models with more confidence.What you will learnUnderstand the fundamentals of deep learning and how it is different from machine learningGet familiarized with Tensorflow, one of the most popular libraries for advanced machine learningIncrease the predictive power of your model using feature engineeringUnderstand the basics of deep learning by solving a digit classification problem of MNISTDemonstrate face generation based on the CelebA database, a promising application of generative modelsApply deep learning to other domains like language modeling, sentiment analysis, and machine translationWho this book is forThis book targets data scientists and machine learning developers who wish to get started with deep learning. If you know what deep learning is but are not quite sure of how to use it, this book will help you as well. An understanding of statistics and data science concepts is required. Some familiarity with Python programming will also be beneficial.

Due to the prevalence of social network service and social media, the problem of cyberbullying has risen to the forefront as a major social issue over the last decade. Internet hate, harassment, cyberstalking, cyberbullying—these terms, which were almost unknown 10 years ago—are in the everyday lexicon of all internet users. Unfortunately, it is becoming increasingly difficult to undertake continuous surveillance of websites as new ones are appearing daily. Methods for automatic detection and mitigation for online bullying have become necessary in order to protect the online user experience. Automatic Cyberbullying Detection: Emerging Research and Opportunities provides innovative insights into online bullying and methods of early identification, mitigation, and prevention of harassing speech and activity. Explanations and reasoning for each of these applied methods are provided as well as their pros and cons when applied to the language of online bullying. Also included are some generalizations of cyberbullying as a phenomenon and how to approach the problem from a practical technology-backed point of view. The content within this publication represents the work of deep learning, language modeling, and web mining. It is designed for academicians, social media moderators, IT consultants, programmers, education administrators, researchers, and professionals and covers topics centered on identification methods and mitigation of internet hate and online harassment.

Build neural network models in text, vision and advanced analytics using PyTorchKey FeaturesLearn PyTorch for implementing cutting-edge deep learning algorithms.Train your neural networks for higher speed and flexibility and learn how to implement them in various scenarios;Cover various advanced neural network architecture such as ResNet, Inception, DenseNet and more with practical examples;Book DescriptionDeep learning powers the most intelligent systems in the world, such as Google Voice, Siri, and Alexa. Advancements in powerful hardware, such as GPUs, software frameworks such as PyTorch, Keras, TensorFlow, and CNTK along with the availability of big data have made it easier to implement solutions to problems in the areas of text, vision, and advanced analytics. This book will get you up and running with one of the most cutting-edge deep learning libraries—PyTorch. PyTorch is grabbing the attention of deep learning researchers and data science professionals due to its accessibility, efficiency and being more native to Python way of development. You'll start off by installing PyTorch, then quickly move on to learn various fundamental blocks that power modern deep learning. You will also learn how to use CNN, RNN, LSTM and other networks to solve real-world problems. This book explains the concepts of various state-of-the-art deep learning architectures, such as ResNet, DenseNet, Inception, and Seq2Seq, without diving deep into the math behind them. You will also learn about GPU computing during the course of the book. You will see how to train a model with PyTorch and dive into complex neural networks such as generative networks for producing text and images. By the end of the book, you'll be able to implement deep learning applications in PyTorch with ease. What you will learnUse PyTorch for GPU-accelerated tensor computationsBuild custom datasets and data loaders for images and test the models using torchvision and torchtextBuild an image classifier by implementing CNN architectures using PyTorchBuild systems that do text classification and language modeling using RNN, LSTM, and GRULearn advanced CNN architectures such as ResNet, Inception, Densenet, and learn how to use them for transfer learningLearn how to mix multiple models for a powerful ensemble modelGenerate new images using GAN's and generate artistic images using style transferWho this book is forThis book is for machine learning engineers, data analysts, data scientists interested in deep learning and are looking to explore implementing advanced algorithms in PyTorch. Some knowledge of machine learning is helpful but not a mandatory need. Working knowledge of Python programming is expected.

The two-volume set LNCS 9623 + 9624 constitutes revised selected papers from the CICLing 2016 conference which took place in Konya, Turkey, in April 2016. The total of 89 papers presented in the two volumes was carefully reviewed and selected from 298 submissions. The book also contains 4 invited papers and a memorial paper on Adam Kilgarriff's Legacy to Computational Linguistics. The papers are organized in the following topical sections: Part I: In memoriam of Adam Kilgarriff; general formalisms; embeddings, language modeling, and sequence labeling; lexical resources and terminology extraction; morphology and part-of-speech tagging; syntax and chunking; named entity recognition; word sense disambiguation and anaphora resolution; semantics, discourse, and dialog. Part II: machine translation and multilingualism; sentiment analysis, opinion mining, subjectivity, and social media; text classification and categorization; information extraction; and applications.

The two-volume set LNCS 9623 + 9624 constitutes revised selected papers from the CICLing 2016 conference which took place in Konya, Turkey, in April 2016. The total of 89 papers presented in the two volumes was carefully reviewed and selected from 298 submissions. The book also contains 4 invited papers and a memorial paper on Adam Kilgarriff's Legacy to Computational Linguistics. The papers are organized in the following topical sections: Part I: In memoriam of Adam Kilgarriff; general formalisms; embeddings, language modeling, and sequence labeling; lexical resources and terminology extraction; morphology and part-of-speech tagging; syntax and chunking; named entity recognition; word sense disambiguation and anaphora resolution; semantics, discourse, and dialog. Part II: machine translation and multilingualism; sentiment analysis, opinion mining, subjectivity, and social media; text classification and categorization; information extraction; and applications.

This book brings together work on Turkish natural language and speech processing over the last 25 years, covering numerous fundamental tasks ranging from morphological processing and language modeling, to full-fledged deep parsing and machine translation, as well as computational resources developed along the way to enable most of this work. Owing to its complex morphology and free constituent order, Turkish has proved to be a fascinating language for natural language and speech processing research and applications.After an overview of the aspects of Turkish that make it challenging for natural language and speech processing tasks, this book discusses in detail the main tasks and applications of Turkish natural language and speech processing. A compendium of the work on Turkish natural language and speech processing, it is a valuable reference for new researchers considering computational work on Turkish, as well as a one-stop resource for commercialand research institutions planning to develop applications for Turkish. It also serves as a blueprint for similar work on other Turkic languages such as Azeri, Turkmen and Uzbek.

Insightful projects to master deep learning and neural network architectures using Python and KerasKey FeaturesExplore deep learning across computer vision, natural language processing (NLP), and image processingDiscover best practices for the training of deep neural networks and their deploymentAccess popular deep learning models as well as widely used neural network architecturesBook DescriptionDeep learning has been gradually revolutionizing every field of artificial intelligence, making application development easier. Python Deep Learning Projects imparts all the knowledge needed to implement complex deep learning projects in the field of computational linguistics and computer vision. Each of these projects is unique, helping you progressively master the subject. You'll learn how to implement a text classifier system using a recurrent neural network (RNN) model and optimize it to understand the shortcomings you might experience while implementing a simple deep learning system. Similarly, you'll discover how to develop various projects, including word vector representation, open domain question answering, and building chatbots using seq-to-seq models and language modeling. In addition to this, you'll cover advanced concepts, such as regularization, gradient clipping, gradient normalization, and bidirectional RNNs, through a series of engaging projects. By the end of this book, you will have gained knowledge to develop your own deep learning systems in a straightforward way and in an efficient way What you will learnSet up a deep learning development environment on Amazon Web Services (AWS)Apply GPU-powered instances as well as the deep learning AMIImplement seq-to-seq networks for modeling natural language processing (NLP)Develop an end-to-end speech recognition systemBuild a system for pixel-wise semantic labeling of an imageCreate a system that generates images and their regionsWho this book is forPython Deep Learning Projects is for you if you want to get insights into deep learning, data science, and artificial intelligence. This book is also for those who want to break into deep learning and develop their own AI projects. It is assumed that you have sound knowledge of Python programming

This book investigates the role that ambiguous evidence can play in the acquisition of syntax. To illustrate this, the book introduces a probabilistic learning model for syntactic parameters that learns a grammar of best fit to the learner's evidence. The model is then applied to a range of cross-linguistic case studies – in Swiss German, Korean, and English – involving child errors, grammatical variability, and implicit negative evidence. Building on earlier work on language modeling, this book is unique for its focus on ambiguous evidence and its careful attention to the effects of parameters interacting with each other. This allows for a novel and principled account of several acquisition puzzles. With its inter-disciplinary approach, this book will be of broad interest to syntacticians, language acquisitionists, and cognitive scientists of language.

About This BookDevelop a sound strategy to solve predictive modelling problems using the most popular machine learning Java libraries.Explore a broad variety of data processing, machine learning, and natural language processing through diagrams, source code, and real-world applicationsThis step-by-step guide will help you solve real-world problems and links neural network theory to their applicationWho This Book Is ForThis course is intended for data scientists and Java developers who want to dive into the exciting world of deep learning. It will get you up and running quickly and provide you with the skills you need to successfully create, customize, and deploy machine learning applications in real life.What You Will LearnGet a practical deep dive into machine learning and deep learning algorithmsExplore neural networks using some of the most popular Deep Learning frameworksDive into Deep Belief Nets and Stacked Denoising Autoencoders algorithmsApply machine learning to fraud, anomaly, and outlier detectionExperiment with deep learning concepts, algorithms, and the toolbox for deep learningSelect and split data sets into training, test, and validation, and explore validation strategiesApply the code generated in practical examples, including weather forecasting and pattern recognitionIn DetailMachine learning applications are everywhere, from self-driving cars, spam detection, document search, and trading strategies, to speech recognitionStarting with an introduction to basic machine learning algorithms, this course takes you further into this vital world of stunning predictive insights and remarkable machine intelligence. This course helps you solve challenging problems in image processing, speech recognition, language modeling. You will discover how to detect anomalies and fraud, and ways to perform activity recognition, image recognition, and text. You will also work with examples such as weather forecasting, disease diagnosis, customer profiling, generalization, extreme machine learning and more. By the end of this course, you will have all the knowledge you need to perform deep learning on your system with varying complexity levels, to apply them to your daily work.The course provides you with highly practical content explaining deep learning with Java, from the following Packt books:Java Deep Learning EssentialsMachine Learning in JavaNeural Network Programming with Java, Second Edition

About This BookLearn to implement various NLP tasks in PythonGain insights into the current and budding research topics of NLPThis is a comprehensive step-by-step guide to help students and researchers create their own projects based on real-life applicationsWho This Book Is ForThis book is for intermediate level developers in NLP with a reasonable knowledge level and understanding of Python.What You Will LearnImplement string matching algorithms and normalization techniquesImplement statistical language modeling techniquesGet an insight into developing a stemmer, lemmatizer, morphological analyzer, and morphological generatorDevelop a search engine and implement POS tagging concepts and statistical modeling concepts involving the n-gram approachFamiliarize yourself with concepts such as the Treebank construct, CFG construction, the CYK chart parsing algorithm, and the Earley chart parsing algorithmDevelop a NER-based system and understand and apply the concepts of sentiment analysisUnderstand and implement the concepts of information retrieval and text summarizationDevelop a discourse analysis system and anaphora resolution-based systemIn DetailNatural Language Processing is one of the fields of computational linguistics and artificial intelligence that is concerned with human-computer interaction. It provides seamless interaction between computers and human beings and gives computers the ability to understand human speech with the help of machine learning.This book will show you how to employ various NLP tasks in Python, and give you an insight into the best practices when designing and building NLP-based applications using Python. It will help you become an expert in no time and help you to create your own NLP projects using NLTK.You will sequentially be guided through applying machine learning tools to develop various models. We'll provide clarity regarding the creation of training data and the implementation of major NLP applications such as named entity recognition, question-answering system, discourse analysis, transliteration, word sense disambiguation, information retrieval, sentiment analysis, text summarization, and anaphora resolution.

Dive into the future of data science and learn how to build the sophisticated algorithms that are fundamental to deep learning and AI with JavaKey Features[•] Go beyond the theory and put Deep Learning into practice with Java[•]Find out how to build a range of Deep Learning algorithms using a range of leading frameworks including DL4J, Theano and Caffe[•] Whether you're a data scientist or Java developer, dive in and find out how to tackle Deep LearningBook DescriptionAI and Deep Learning are transforming the way we understand software, making computers more intelligent than we could even imagine just a decade ago. Deep Learning algorithms are being used across a broad range of industries – as the fundamental driver of AI, being able to tackle Deep Learning is going to a vital and valuable skill not only within the tech world but also for the wider global economy that depends upon knowledge and insight for growth and success. It's something that's moving beyond the realm of data science – if you're a Java developer, this book gives you a great opportunity to expand your skillset. Starting with an introduction to basic machine learning algorithms, to give you a solid foundation, Deep Learning with Java takes you further into this vital world of stunning predictive insights and remarkable machine intelligence. Once you've got to grips with the fundamental mathematical principles, you'll start exploring neural networks and identify how to tackle challenges in large networks using advanced algorithms. You will learn how to use the DL4J library and apply Deep Learning to a range of real-world use cases. Featuring further guidance and insights to help you solve challenging problems in image processing, speech recognition, language modeling, this book will make you rethink what you can do with Java, showing you how to use it for truly cutting-edge predictive insights. As a bonus, you'll also be able to get to grips with Theano and Caffe, two of the most important tools in Deep Learning today. By the end of the book, you'll be ready to tackle Deep Learning with Java. Wherever you've come from – whether you're a data scientist or Java developer – you will become a part of the Deep Learning revolution!What you will learn[•] Get a practical deep dive into machine learning and deep learning algorithms[•] Implement machine learning algorithms related to deep learning[•] Explore neural networks using some of the most popular Deep Learning frameworks[•] Dive into Deep Belief Nets and Stacked Denoising Autoencoders algorithms[•] Discover more deep learning algorithms with Dropout and Convolutional Neural Networks[•] Gain an insight into the deep learning library DL4J and its practical uses[•] Get to know device strategies to use deep learning algorithms and libraries in the real world[•] Explore deep learning further with Theano and CaffeWho this book is forThis book is intended for data scientists and Java developers who want to dive into the exciting world of deep learning. It would also be good for machine learning users who intend to leverage deep learning in their projects, working within a big data environment.

The Common Core State Standards require students to do more with knowledge and language than ever before. Rather than be mere consumers of knowledge, students must now become creators, critics, and communicators of ideas across disciplines. Yet in order to take on these new and exciting roles, many students need daily teaching with an extra emphasis on accelerating their academic communication skills. Common Core Standards in Diverse Classrooms: Essential Practices for Developing Academic Language and Disciplinary Literacy describes seven research-based teaching practices for developing complex language and literacy skills across grade levels and disciplines: using complex texts, fortifying complex output, fostering academic interaction, clarifying complex language, modeling, guiding, and designing instruction. Most important, you will find clear descriptions and examples of how these essential practices can-;and should-;be woven together in real lessons. The book: Clarifieshow to support the learning of complex language that students need for reaching Common Core and other standardsProvides practical ways to realize the instructional shifts needed with the implementation of new standards in diverse classroomsIncludes frameworks and descriptions on how to develop students'complex language, speaking, and writingHelps maximize strategies and tools for building system-wide capacity for sustained growth in the practicesCommon Core Standards in Diverse Classrooms is a concise guide for helping us improve our practices to strengthen two vital pillars that support student learning: academic language and disciplinary literacy.