Download or read book Advanced Deep Learning with Python written by Ivan Vasilev and published by Packt Publishing Ltd. This book was released on 2019-12-12 with total page 456 pages. Available in PDF, EPUB and Kindle. Book excerpt: Gain expertise in advanced deep learning domains such as neural networks, meta-learning, graph neural networks, and memory augmented neural networks using the Python ecosystem Key FeaturesGet to grips with building faster and more robust deep learning architecturesInvestigate and train convolutional neural network (CNN) models with GPU-accelerated libraries such as TensorFlow and PyTorchApply deep neural networks (DNNs) to computer vision problems, NLP, and GANsBook Description In order to build robust deep learning systems, you’ll need to understand everything from how neural networks work to training CNN models. In this book, you’ll discover newly developed deep learning models, methodologies used in the domain, and their implementation based on areas of application. You’ll start by understanding the building blocks and the math behind neural networks, and then move on to CNNs and their advanced applications in computer vision. You'll also learn to apply the most popular CNN architectures in object detection and image segmentation. Further on, you’ll focus on variational autoencoders and GANs. You’ll then use neural networks to extract sophisticated vector representations of words, before going on to cover various types of recurrent networks, such as LSTM and GRU. You’ll even explore the attention mechanism to process sequential data without the help of recurrent neural networks (RNNs). Later, you’ll use graph neural networks for processing structured data, along with covering meta-learning, which allows you to train neural networks with fewer training samples. Finally, you’ll understand how to apply deep learning to autonomous vehicles. By the end of this book, you’ll have mastered key deep learning concepts and the different applications of deep learning models in the real world. What you will learnCover advanced and state-of-the-art neural network architecturesUnderstand the theory and math behind neural networksTrain DNNs and apply them to modern deep learning problemsUse CNNs for object detection and image segmentationImplement generative adversarial networks (GANs) and variational autoencoders to generate new imagesSolve natural language processing (NLP) tasks, such as machine translation, using sequence-to-sequence modelsUnderstand DL techniques, such as meta-learning and graph neural networksWho this book is for This book is for data scientists, deep learning engineers and researchers, and AI developers who want to further their knowledge of deep learning and build innovative and unique deep learning projects. Anyone looking to get to grips with advanced use cases and methodologies adopted in the deep learning domain using real-world examples will also find this book useful. Basic understanding of deep learning concepts and working knowledge of the Python programming language is assumed.

Download or read book Speech Language Processing written by Dan Jurafsky and published by Pearson Education India. This book was released on 2000-09 with total page 912 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Deep Learning Essentials written by Anurag Bhardwaj and published by Packt Publishing Ltd. This book was released on 2018-01-30 with total page 271 pages. Available in PDF, EPUB and Kindle. Book excerpt: Get to grips with the essentials of deep learning by leveraging the power of Python Key Features Your one-stop solution to get started with the essentials of deep learning and neural network modeling Train different kinds of neural networks to tackle various problems in Natural Language Processing, computer vision, speech recognition, and more Covers popular Python libraries such as Tensorflow, Keras, and more, along with tips on training, deploying and optimizing your deep learning models in the best possible manner Book Description Deep Learning a trending topic in the field of Artificial Intelligence today and can be considered to be an advanced form of machine learning, which is quite tricky to master. This book will help you take your first steps in training efficient deep learning models and applying them in various practical scenarios. You will model, train, and deploy different kinds of neural networks such as Convolutional Neural Network, Recurrent Neural Network, and will see some of their applications in real-world domains including computer vision, natural language processing, speech recognition, and so on. You will build practical projects such as chatbots, implement reinforcement learning to build smart games, and develop expert systems for image captioning and processing. Popular Python library such as TensorFlow is used in this book to build the models. This book also covers solutions for different problems you might come across while training models, such as noisy datasets, small datasets, and more. This book does not assume any prior knowledge of deep learning. By the end of this book, you will have a firm understanding of the basics of deep learning and neural network modeling, along with their practical applications. What you will learn Get to grips with the core concepts of deep learning and neural networks Set up deep learning library such as TensorFlow Fine-tune your deep learning models for NLP and Computer Vision applications Unify different information sources, such as images, text, and speech through deep learning Optimize and fine-tune your deep learning models for better performance Train a deep reinforcement learning model that plays a game better than humans Learn how to make your models get the best out of your GPU or CPU Who this book is for Aspiring data scientists and machine learning experts who have limited or no exposure to deep learning will find this book to be very useful. If you are looking for a resource that gets you up and running with the fundamentals of deep learning and neural networks, this book is for you. As the models in the book are trained using the popular Python-based libraries such as Tensorflow and Keras, it would be useful to have sound programming knowledge of Python.

Download or read book Large Language Models written by Uday Kamath and published by Springer Nature. This book was released on 2024 with total page 496 pages. Available in PDF, EPUB and Kindle. Book excerpt: Large Language Models (LLMs) have emerged as a cornerstone technology, transforming how we interact with information and redefining the boundaries of artificial intelligence. LLMs offer an unprecedented ability to understand, generate, and interact with human language in an intuitive and insightful manner, leading to transformative applications across domains like content creation, chatbots, search engines, and research tools. While fascinating, the complex workings of LLMs -- their intricate architecture, underlying algorithms, and ethical considerations -- require thorough exploration, creating a need for a comprehensive book on this subject. This book provides an authoritative exploration of the design, training, evolution, and application of LLMs. It begins with an overview of pre-trained language models and Transformer architectures, laying the groundwork for understanding prompt-based learning techniques. Next, it dives into methods for fine-tuning LLMs, integrating reinforcement learning for value alignment, and the convergence of LLMs with computer vision, robotics, and speech processing. The book strongly emphasizes practical applications, detailing real-world use cases such as conversational chatbots, retrieval-augmented generation (RAG), and code generation. These examples are carefully chosen to illustrate the diverse and impactful ways LLMs are being applied in various industries and scenarios. Readers will gain insights into operationalizing and deploying LLMs, from implementing modern tools and libraries to addressing challenges like bias and ethical implications. The book also introduces the cutting-edge realm of multimodal LLMs that can process audio, images, video, and robotic inputs. With hands-on tutorials for applying LLMs to natural language tasks, this thorough guide equips readers with both theoretical knowledge and practical skills for leveraging the full potential of large language models. This comprehensive resource is appropriate for a wide audience: students, researchers and academics in AI or NLP, practicing data scientists, and anyone looking to grasp the essence and intricacies of LLMs.

Download or read book Challenges in Large Language Model Development and AI Ethics written by Gupta, Brij and published by IGI Global. This book was released on 2024-08-15 with total page 521 pages. Available in PDF, EPUB and Kindle. Book excerpt: The development of large language models has resulted in artificial intelligence advancements promising transformations and benefits across various industries and sectors. However, this progress is not without its challenges. The scale and complexity of these models pose significant technical hurdles, including issues related to bias, transparency, and data privacy. As these models integrate into decision-making processes, ethical concerns about their societal impact, such as potential job displacement or harmful stereotype reinforcement, become more urgent. Addressing these challenges requires a collaborative effort from business owners, computer engineers, policymakers, and sociologists. Fostering effective research for solutions to address AI ethical challenges may ensure that large language model developments benefit society in a positive way. Challenges in Large Language Model Development and AI Ethics addresses complex ethical dilemmas and challenges of the development of large language models and artificial intelligence. It analyzes ethical considerations involved in the design and implementation of large language models, while exploring aspects like bias, accountability, privacy, and social impacts. This book covers topics such as law and policy, model architecture, and machine learning, and is a useful resource for computer engineers, sociologists, policymakers, business owners, academicians, researchers, and scientists.

Download or read book Decoding Large Language Models written by Irena Cronin and published by Packt Publishing Ltd. This book was released on 2024-10-31 with total page 396 pages. Available in PDF, EPUB and Kindle. Book excerpt: Explore the architecture, development, and deployment strategies of large language models to unlock their full potential Key Features Gain in-depth insight into LLMs, from architecture through to deployment Learn through practical insights into real-world case studies and optimization techniques Get a detailed overview of the AI landscape to tackle a wide variety of AI and NLP challenges Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionEver wondered how large language models (LLMs) work and how they're shaping the future of artificial intelligence? Written by a renowned author and AI, AR, and data expert, Decoding Large Language Models is a combination of deep technical insights and practical use cases that not only demystifies complex AI concepts, but also guides you through the implementation and optimization of LLMs for real-world applications. You’ll learn about the structure of LLMs, how they're developed, and how to utilize them in various ways. The chapters will help you explore strategies for improving these models and testing them to ensure effective deployment. Packed with real-life examples, this book covers ethical considerations, offering a balanced perspective on their societal impact. You’ll be able to leverage and fine-tune LLMs for optimal performance with the help of detailed explanations. You’ll also master techniques for training, deploying, and scaling models to be able to overcome complex data challenges with confidence and precision. This book will prepare you for future challenges in the ever-evolving fields of AI and NLP. By the end of this book, you’ll have gained a solid understanding of the architecture, development, applications, and ethical use of LLMs and be up to date with emerging trends, such as GPT-5.What you will learn Explore the architecture and components of contemporary LLMs Examine how LLMs reach decisions and navigate their decision-making process Implement and oversee LLMs effectively within your organization Master dataset preparation and the training process for LLMs Hone your skills in fine-tuning LLMs for targeted NLP tasks Formulate strategies for the thorough testing and evaluation of LLMs Discover the challenges associated with deploying LLMs in production environments Develop effective strategies for integrating LLMs into existing systems Who this book is for If you’re a technical leader working in NLP, an AI researcher, or a software developer interested in building AI-powered applications, this book is for you. To get the most out of this book, you should have a foundational understanding of machine learning principles; proficiency in a programming language such as Python; knowledge of algebra and statistics; and familiarity with natural language processing basics.

Download or read book Computer Vision ECCV 2020 written by Andrea Vedaldi and published by Springer Nature. This book was released on 2020-11-20 with total page 830 pages. Available in PDF, EPUB and Kindle. Book excerpt: The 30-volume set, comprising the LNCS books 12346 until 12375, constitutes the refereed proceedings of the 16th European Conference on Computer Vision, ECCV 2020, which was planned to be held in Glasgow, UK, during August 23-28, 2020. The conference was held virtually due to the COVID-19 pandemic. The 1360 revised papers presented in these proceedings were carefully reviewed and selected from a total of 5025 submissions. The papers deal with topics such as computer vision; machine learning; deep neural networks; reinforcement learning; object recognition; image classification; image processing; object detection; semantic segmentation; human pose estimation; 3d reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; object recognition; motion estimation.

Download or read book Explainable AI for Practitioners written by Michael Munn and published by "O'Reilly Media, Inc.". This book was released on 2022-10-31 with total page 284 pages. Available in PDF, EPUB and Kindle. Book excerpt: Most intermediate-level machine learning books focus on how to optimize models by increasing accuracy or decreasing prediction error. But this approach often overlooks the importance of understanding why and how your ML model makes the predictions that it does. Explainability methods provide an essential toolkit for better understanding model behavior, and this practical guide brings together best-in-class techniques for model explainability. Experienced machine learning engineers and data scientists will learn hands-on how these techniques work so that you'll be able to apply these tools more easily in your daily workflow. This essential book provides: A detailed look at some of the most useful and commonly used explainability techniques, highlighting pros and cons to help you choose the best tool for your needs Tips and best practices for implementing these techniques A guide to interacting with explainability and how to avoid common pitfalls The knowledge you need to incorporate explainability in your ML workflow to help build more robust ML systems Advice about explainable AI techniques, including how to apply techniques to models that consume tabular, image, or text data Example implementation code in Python using well-known explainability libraries for models built in Keras and TensorFlow 2.0, PyTorch, and HuggingFace

Download or read book Graph Representation Learning written by William L. William L. Hamilton and published by Springer Nature. This book was released on 2022-06-01 with total page 141 pages. Available in PDF, EPUB and Kindle. Book excerpt: Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D vision, recommender systems, question answering, and social network analysis. This book provides a synthesis and overview of graph representation learning. It begins with a discussion of the goals of graph representation learning as well as key methodological foundations in graph theory and network analysis. Following this, the book introduces and reviews methods for learning node embeddings, including random-walk-based methods and applications to knowledge graphs. It then provides a technical synthesis and introduction to the highly successful graph neural network (GNN) formalism, which has become a dominant and fast-growing paradigm for deep learning with graph data. The book concludes with a synthesis of recent advancements in deep generative models for graphs—a nascent but quickly growing subset of graph representation learning.

Download or read book Redefining Security With Cyber AI written by Omar, Marwan and published by IGI Global. This book was released on 2024-07-17 with total page 287 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the rapidly evolving digital landscape, the importance of cybersecurity has never been more critical. With the increasing sophistication of cyber threats, traditional security measures often fall short in providing adequate protection. Cyber artificial intelligence (AI) offers advanced capabilities to detect, prevent, and respond to attacks in real time. As cyber threats continue to grow in complexity and frequency, the integration of AI into cybersecurity frameworks is not just advantageous but essential for maintaining robust and resilient defenses. Redefining Security With Cyber AI delves into the profound transformation of security paradigms brought about by the advent of AI. This book explores the intricate dance between the ever-expanding frontiers of digital technology and the AI-driven mechanisms that aim to safeguard them. Covering topics such as artificial neural networks, intrusion detection, and large language models, this book is an excellent resource for cybersecurity professionals, AI and machine learning researchers, IT executives and managers, policy makers and regulators, postgraduate students and educators, academicians, and more.

Download or read book Web Information Systems Engineering WISE 2023 written by Feng Zhang and published by Springer Nature. This book was released on 2023-10-21 with total page 953 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the 24th International Conference on Web Information Systems Engineering, WISE 2023, held in Melbourne, Victoria, Australia, in October 2023. The 33 full and 40 short papers were carefully reviewed and selected from 137 submissions. They were organized in topical sections as follows: text and sentiment analysis; question answering and information retrieval; social media and news analysis; security and privacy; web technologies; graph embeddings and link predictions; predictive analysis and machine learning; recommendation systems; natural language processing (NLP) and databases; data analysis and optimization; anomaly and threat detection; streaming data; miscellaneous; explainability and scalability in AI.

Download or read book Dependency Parsing written by Sandra Kübler and published by Morgan & Claypool Publishers. This book was released on 2009 with total page 128 pages. Available in PDF, EPUB and Kindle. Book excerpt: Dependency-based methods for syntactic parsing have become increasingly popular in natural language processing in recent years. This book gives a thorough introduction to the methods that are most widely used today. After an introduction to dependency grammar and dependency parsing, followed by a formal characterization of the dependency parsing problem, the book surveys the three major classes of parsing models that are in current use: transition-based, graph-based, and grammar-based models. It continues with a chapter on evaluation and one on the comparison of different methods, and it closes with a few words on current trends and future prospects of dependency parsing. The book presupposes a knowledge of basic concepts in linguistics and computer science, as well as some knowledge of parsing methods for constituency-based representations. Table of Contents: Introduction / Dependency Parsing / Transition-Based Parsing / Graph-Based Parsing / Grammar-Based Parsing / Evaluation / Comparison / Final Thoughts

Download or read book Building LLM Powered Applications written by Valentina Alto and published by Packt Publishing Ltd. This book was released on 2024-05-22 with total page 343 pages. Available in PDF, EPUB and Kindle. Book excerpt: Get hands-on with GPT 3.5, GPT 4, LangChain, Llama 2, Falcon LLM and more, to build LLM-powered sophisticated AI applications Key Features Embed LLMs into real-world applications Use LangChain to orchestrate LLMs and their components within applications Grasp basic and advanced techniques of prompt engineering Book 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 learn Explore the core components of LLM architecture, including encoder-decoder blocks and embeddings Understand the unique features of LLMs like GPT-3.5/4, Llama 2, and Falcon LLM Use AI orchestrators like LangChain, with Streamlit for the frontend Get familiar with LLM components such as memory, prompts, and tools Learn how to use non-parametric knowledge and vector databases Understand the implications of LFMs for AI research and industry applications Customize your LLMs with fine tuning Learn about the ethical implications of LLM-powered applications Who 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.

Download or read book Pattern Recognition ICPR International Workshops and Challenges written by Alberto Del Bimbo and published by Springer Nature. This book was released on 2021-02-22 with total page 753 pages. Available in PDF, EPUB and Kindle. Book excerpt: This 8-volumes set constitutes the refereed of the 25th International Conference on Pattern Recognition Workshops, ICPR 2020, held virtually in Milan, Italy and rescheduled to January 10 - 11, 2021 due to Covid-19 pandemic. The 416 full papers presented in these 8 volumes were carefully reviewed and selected from about 700 submissions. The 46 workshops cover a wide range of areas including machine learning, pattern analysis, healthcare, human behavior, environment, surveillance, forensics and biometrics, robotics and egovision, cultural heritage and document analysis, retrieval, and women at ICPR2020.

Download or read book Advances in Data and Information Sciences written by Shailesh Tiwari and published by Springer Nature. This book was released on 2024-01-02 with total page 547 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book gathers a collection of high-quality peer-reviewed research papers presented at the 5th International Conference on Data and Information Sciences (ICDIS 2023), held at Raja Balwant Singh Engineering Technical Campus, Agra, India, on June 16–17, 2023. The book covers all aspects of computational sciences and information security, including central topics like artificial intelligence, cloud computing, and big data. Highlighting the latest developments and technical solutions, it shows readers from the computer industry how to capitalize on key advances in next-generation computer and communication technology.

Download or read book The Semantic Web written by Albert Meroño Peñuela and published by Springer Nature. This book was released on with total page 367 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Hybrid Intelligent Systems for Information Retrieval written by Anuradha D Thakare and published by CRC Press. This book was released on 2022-11-22 with total page 253 pages. Available in PDF, EPUB and Kindle. Book excerpt: Hybrid Intelligent Systems for Information Retrieval covers three areas along with the introduction to Intelligent IR, i.e., Optimal Information Retrieval Using Evolutionary Approaches, Semantic Search for Web Information Retrieval, and Natural Language Processing for Information Retrieval. • Talks about the design, implementation, and performance issues of the hybrid intelligent information retrieval system in one book • Gives a clear insight into challenges and issues in designing a hybrid information retrieval system • Includes case studies on structured and unstructured data for hybrid intelligent information retrieval • Provides research directions for the design and development of intelligent search engines This book is aimed primarily at graduates and researchers in the information retrieval domain.