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  • 1
    Online Resource
    Online Resource
    The Generative AI Conference : practical insights and proven playbooks (2023)
    [Place of publication not identified] : Packt Publishing
    Details
    ISBN: 9781835884041 , 1835884040
    Language: English
    Pages: 1 online resource (1 video file (6 hr., 30 min.)) , sound, color.
    Edition: [First edition].
    DDC: 006.3
    Keywords: Artificial intelligence Congresses ; Instructional films ; Nonfiction films ; Internet videos
    Abstract: Generative AI is dominating discussions on AI. It is a superpower that every tech professional must harness, but harnessing its power takes knowledge, strategy, and vision. We present you with the recordings from our acclaimed Generative AI conference in one comprehensive video collection. We've packaged the invaluable insights and techniques shared by bestselling AI authors, innovators, and practitioners from Meta, Microsoft, Deloitte, JPMorgan, NVIDIA, Salesforce, and more. These talks and tech sessions provide unique perspectives on realizing generative AI's immense potential. Let the brightest minds equip you with the knowledge to help you put generative AI to work for your unique needs. What you will learn Panel Discussion: Put Generative AI to Work! Navigating the Hype and Shaping the Future with Gen AI Revelations from 30 AI Visionaries About Generative AI's Future in Business The Large Language Model Revolution in Recommender Systems Personalization Fireside Chat: Navigating Responsible AI Development Amidst LLMs Innovation Tackling OWASP's Top 10 Risks Head On Audience This video collection caters to forward-thinking technology professionals and leaders in various industries who aspire to leverage generative AI for business transformation. It is designed for product leaders interested in integrating generative AI into their overall business strategy, AI specialists and researchers in search of the latest techniques for developing custom generative models, data scientists aiming to uncover insights and automate processes using generative AI, as well as business owners and entrepreneurs exploring new revenue opportunities powered by generative AI. About the Authors Clint Bodungen: Clint Bodungen is a globally recognized cybersecurity authority and brings over a quarter-century of experience to the table. A veteran of the United States Air Force and seasoned professional at notable cybersecurity firms like Symantec, Kaspersky Lab, and Booz Allen Hamilton, he is renowned for his innovative approaches in the field. Clint has contributed to the field as the author of two insightful books: 'Hacking Exposed: Industrial Control Systems' and 'ChatGPT for Cybersecurity Cookbook.' These works underscore his wide-ranging knowledge and expertise in cybersecurity, establishing him as a thought leader in this ever-evolving field. Denis Rothman: Expert in AI Transformers including ChatGPT/GPT-4, Bestselling Author John K. Thompson: Bestselling Author, Innovator in Data, AI, & Technology Amit Kumar: Sr. Enterprise Solutions Architect - Generative AI, NVIDIA Vinoo Ganesh: Speaker, Technologist, and Startup Advisor Amey Dharwadker: Engineering Leader, Machine Learning at Meta Bill Schmarzo: Dean of Big Data, Bestselling AI author Maria Parysz: AI Practitioner, CEO and Owner at LogicAI & RecoAI, ElephantAI Sadid Hasan: AI Lead, Microsoft Andreas Welsch: Chief AI Strategist, Intelligence Briefing Aleksander Molak: Causality Advocate, Bestselling Author, AI Researcher & Strategist Shyam Varan Nath: Specialist Leader - AI & Analytics, Deloitte Sebastian Raschka: Sebastian Raschka is an Assistant Professor of Statistics at the University of Wisconsin-Madison focusing on machine learning and deep learning research. As Lead AI Educator at Grid AI, Sebastian plans to continue following his passion for helping people get into machine learning and artificial intelligence.
    Note: Online resource; title from title details screen (O'Reilly, viewed Decenber 19, 2023)
    URL: lizenzpflichtig
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    Online Resource
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  • 2
    Online Resource
    Online Resource
    Interpretable machine learning with Python : build explainable, fair, and robust high-performance models with hands-on, real-world examples (2023)
    Birmingham, UK : Packt Publishing Ltd.
    Details
    ISBN: 9781803235424
    Language: English
    Pages: 1 online resource (606 pages) , illustrations
    Edition: Second edition.
    Series Statement: Expert insight
    DDC: 005.13/3
    Keywords: Machine learning ; Python (Computer program language) ; Data mining
    Abstract: Interpretable Machine Learning with Python, Second Edition, brings to light the key concepts of interpreting machine learning models by analyzing real-world data, providing you with a wide range of skills and tools to decipher the results of even the most complex models. Build your interpretability toolkit with several use cases, from flight delay prediction to waste classification to COMPAS risk assessment scores. This book is full of useful techniques, introducing them to the right use case. Learn traditional methods, such as feature importance and partial dependence plots to integrated gradients for NLP interpretations and gradient-based attribution methods, such as saliency maps. In addition to the step-by-step code, you’ll get hands-on with tuning models and training data for interpretability by reducing complexity, mitigating bias, placing guardrails, and enhancing reliability. By the end of the book, you’ll be confident in tackling interpretability challenges with black-box models using tabular, language, image, and time series data. This book is for data scientists, machine learning developers, machine learning engineers, MLOps engineers, and data stewards who have an increasingly critical responsibility to explain how the artificial intelligence systems they develop work, their impact on decision making, and how they identify and manage bias. It’s also a useful resource for self-taught ML enthusiasts and beginners who want to go deeper into the subject matter, though a good grasp of the Python programming language is needed to implement the examples.
    Note: Includes bibliographical references and index
    URL: lizenzpflichtig
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  • 3
    Online Resource
    Online Resource
    Transformers for Natural Language Processing (2021)
    [Erscheinungsort nicht ermittelbar] : Packt Publishing | Boston, MA : Safari
    Details
    ISBN: 9781800565791
    Language: English
    Pages: 1 online resource (384 pages)
    Edition: 1st edition
    Keywords: Electronic books ; local
    Abstract: Become an AI language understanding expert by mastering the quantum leap of Transformer neural network models Key Features Build 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 models Go through hands-on applications in Python using Google Colaboratory Notebooks with nothing to install on a local machine Learn training tips and alternative language understanding methods to illustrate important key concepts Book Description The 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 learn Use the latest pretrained transformer models Grasp the workings of the original Transformer, GPT-2, BERT, T5, and other transformer models Create language understanding Python programs using concepts that outperform classical deep learning models Use a variety of NLP platforms, including Hugging Face, Trax, and AllenNLP Apply Python, TensorFlow, and Keras programs to sentiment analysis, text summarization, speech recognition, machine translations, and more Measure the productivity of key transformers to define their scope, potential, and...
    Note: Online resource; Title from title page (viewed January 29, 2021) , Mode of access: World Wide Web.
    URL: lizenzpflichtig
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  • 4
    Online Resource
    Online Resource
    Artificial Intelligence By Example (2020)
    [Erscheinungsort nicht ermittelbar] : Packt Publishing | Boston, MA : Safari
    Details
    Language: English
    Pages: 1 online resource (578 pages)
    Edition: 2nd edition
    Keywords: Electronic books ; local
    Abstract: Understand the fundamentals and develop your own AI solutions in this updated edition packed with many new examples Key Features AI-based examples to guide you in designing and implementing machine intelligence Build machine intelligence from scratch using artificial intelligence examples Develop machine intelligence from scratch using real artificial intelligence Book Description AI has the potential to replicate humans in every field. Artificial Intelligence By Example, Second Edition serves as a starting point for you to understand how AI is built, with the help of intriguing and exciting examples. This book will make you an adaptive thinker and help you apply concepts to real-world scenarios. Using some of the most interesting AI examples, right from computer programs such as a simple chess engine to cognitive chatbots, you will learn how to tackle the machine you are competing with. You will study some of the most advanced machine learning models, understand how to apply AI to blockchain and Internet of Things (IoT), and develop emotional quotient in chatbots using neural networks such as recurrent neural networks (RNNs) and convolutional neural networks (CNNs). This edition also has new examples for hybrid neural networks, combining reinforcement learning (RL) and deep learning (DL), chained algorithms, combining unsupervised learning with decision trees, random forests, combining DL and genetic algorithms, conversational user interfaces (CUI) for chatbots, neuromorphic computing, and quantum computing. By the end of this book, you will understand the fundamentals of AI and have worked through a number of examples that will help you develop your AI solutions. Apply k-nearest neighbors (KNN) to language translations and explore the opportunities in Google Translate Understand chained algorithms combining unsupervised learning with decision trees Solve the XOR problem with feedforward neural networks (FNN) and build its architecture to represent a data flow graph Learn about meta learning models with hybrid neural networks Create a chatbot and optimize its emotional intelligence deficiencies with tools such as Small Talk and data logging Building conversational user interfaces (CUI) for chatbots Writing genetic algorithms that optimize deep learning neural networks Build quantum computing circuits Who this book is for Developers and those interested in AI, who want to understand the fundamentals of Artificial Intelligence and implement the...
    Note: Online resource; Title from title page (viewed February 28, 2020) , Mode of access: World Wide Web.
    URL: lizenzpflichtig
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    Online Resource
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  • 5
    Online Resource
    Online Resource
    Hands-On Explainable AI (XAI) with Python (2020)
    [Erscheinungsort nicht ermittelbar] : Packt Publishing | Boston, MA : Safari
    Details
    ISBN: 9781800208131
    Language: English
    Pages: 1 online resource (454 pages)
    Edition: 1st edition
    Keywords: Electronic books ; local
    Abstract: Resolve the black box models in your AI applications to make them fair, trustworthy, and secure. Familiarize yourself with the basic principles and tools to deploy Explainable AI (XAI) into your apps and reporting interfaces. Key Features Learn explainable AI tools and techniques to process trustworthy AI results Understand how to detect, handle, and avoid common issues with AI ethics and bias Integrate fair AI into popular apps and reporting tools to deliver business value using Python and associated tools Book Description Effectively translating AI insights to business stakeholders requires careful planning, design, and visualization choices. Describing the problem, the model, and the relationships among variables and their findings are often subtle, surprising, and technically complex. Hands-On Explainable AI (XAI) with Python will see you work with specific hands-on machine learning Python projects that are strategically arranged to enhance your grasp on AI results analysis. You will be building models, interpreting results with visualizations, and integrating XAI reporting tools and different applications. You will build XAI solutions in Python, TensorFlow 2, Google Cloud's XAI platform, Google Colaboratory, and other frameworks to open up the black box of machine learning models. The book will introduce you to several open-source XAI tools for Python that can be used throughout the machine learning project life cycle. You will learn how to explore machine learning model results, review key influencing variables and variable relationships, detect and handle bias and ethics issues, and integrate predictions using Python along with supporting the visualization of machine learning models into user explainable interfaces. By the end of this AI book, you will possess an in-depth understanding of the core concepts of XAI. What you will learn Plan for XAI through the different stages of the machine learning life cycle Estimate the strengths and weaknesses of popular open-source XAI applications Examine how to detect and handle bias issues in machine learning data Review ethics considerations and tools to address common problems in machine learning data Share XAI design and visualization best practices Integrate explainable AI results using Python models Use XAI toolkits for Python in machine learning life cycles to solve business problems Who this book is for This book is not an introduction to Python programming or machine learning concepts. Yo...
    Note: Online resource; Title from title page (viewed July 31, 2020) , Mode of access: World Wide Web.
    URL: lizenzpflichtig
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  • 6
    Online Resource
    Online Resource
    Artificial intelligence by example : develop machine intelligence from scratch using real artificial intelligence use cases (2018)
    Birmingham, UK : Packt Publishing
    Details
    ISBN: 9781788990028 , 1788990021
    Language: English
    Pages: 1 online resource (1 volume) , illustrations
    Keywords: Artificial intelligence ; Data processing ; Application software ; Development ; Python (Computer program language) ; Cloud computing ; Electronic books ; Electronic books ; local
    Abstract: Be an adaptive thinker that leads the way to Artificial Intelligence About This Book AI-based examples to guide you in designing and implementing machine intelligence Develop your own method for future AI solutions Acquire advanced AI, machine learning, and deep learning design skills Who This Book Is For Artificial Intelligence by Example is a simple, explanatory, and descriptive guide for junior developers, experienced developers, technology consultants, and those interested in AI who want to understand the fundamentals of Artificial Intelligence and implement it practically by devising smart solutions. Prior experience with Python and statistical knowledge is essential to make the most out of this book. What You Will Learn Use adaptive thinking to solve real-life AI case studies Rise beyond being a modern-day factory code worker Acquire advanced AI, machine learning, and deep learning designing skills Learn about cognitive NLP chatbots, quantum computing, and IoT and blockchain technology Understand future AI solutions and adapt quickly to them Develop out-of-the-box thinking to face any challenge the market presents In Detail Artificial Intelligence has the potential to replicate humans in every field. This book serves as a starting point for you to understand how AI is built, with the help of intriguing examples and case studies. Artificial Intelligence By Example will make you an adaptive thinker and help you apply concepts to real-life scenarios. Using some of the most interesting AI examples, right from a simple chess engine to a cognitive chatbot, you will learn how to tackle the machine you are competing with. You will study some of the most advanced machine learning models, understand how to apply AI to blockchain and IoT, and develop emotional quotient in chatbots using neural networks. You will move on to designing AI solutions in a simple manner rather than get confused by complex architectures and techniques. This comprehensive guide will be a starter kit for you to develop AI applications on your own. By the end of this book, will have understood the fundamentals of AI and worked through a number of case studies that will help you develop business vision. Style and approach An easy-to-follow step by step guide which will help you get to grips with real world application of Artificial Intelligence Downloading the example code for this book You can download the example code files for all Packt books you have purchased from your a...
    Note: Includes bibliographical references. - Description based on online resource; title from title page (Safari, viewed June 27, 2018)
    URL: https://learning.oreilly.com/library/view/-/9781788990547/?ar
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  • 7
    Online Resource
    Online Resource
    Transformers for natural language processing : build, train, and fine-tuning deep neural network architectures for NLP with Python, PyTorch, TensorFlow, BERT, and GPT-3 (2022)
    [Birmingham, United Kingdom] : Packt Publishing
    Details
    ISBN: 9781803247335
    Language: English
    Pages: 1 online resource (564 pages) , illustrations
    Edition: Second edition.
    Series Statement: Expert insight
    Parallel Title: Erscheint auch als Rothman, Denis Transformers for natural language processing
    DDC: 006.3
    RVK:
    ST 306
    Keywords: Artificial intelligence Data processing ; Artificial intelligence Computer programs ; Python (Computer program language) ; Cloud computing ; Intelligence artificielle ; Informatique ; Intelligence artificielle ; Logiciels ; Python (Langage de programmation) ; Infonuagique ; Electronic books ; Natürliche Sprache ; Deep learning
    Abstract: Transformers are a game-changer for natural language understanding (NLU) and have become one of the pillars of artificial intelligence. Transformers for Natural Language Processing, 2nd Edition, investigates deep learning for machine translations, speech-to-text, text-to-speech, language modeling, question-answering, and many more NLP domains with transformers. An Industry 4.0 AI specialist needs to be adaptable; knowing just one NLP platform is not enough anymore. Different platforms have different benefits depending on the application, whether it's cost, flexibility, ease of implementation, results, or performance. In this book, we analyze numerous use cases with Hugging Face, Google Trax, OpenAI, and AllenNLP. This book takes transformers' capabilities further by combining multiple NLP techniques, such as sentiment analysis, named entity recognition, and semantic role labeling, to analyze complex use cases, such as dissecting fake news on Twitter. Also, see how transformers can create code using just a brief description. By the end of this NLP book, you will understand transformers from a cognitive science perspective and be proficient in applying pretrained transformer models to various datasets.
    Note: Includes index
    URL: lizenzpflichtig
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  • 8
    Online Resource
    Online Resource
    LLMs under the hood : building models for your unique use cases (2024)
    [Place of publication not identified] : Packt Publishing
    Details
    ISBN: 9781835883549 , 1835883540
    Language: English
    Pages: 1 online resource (1 video file (4 hr., 2 min.)) , sound, color.
    Edition: [First edition].
    DDC: 006.3/5
    Keywords: Natural language processing (Computer science) ; Artificial intelligence ; Traitement automatique des langues naturelles ; Intelligence artificielle ; artificial intelligence ; Instructional films ; Nonfiction films ; Internet videos ; Films de formation ; Films autres que de fiction ; Vidéos sur Internet
    Abstract: This in-depth masterclass provides end-to-end coverage of developing enterprise-grade LLMs tailored to your unique use cases. Led by experts Maxime Labonne, Dennis Rothman, and Abi Aryan, this video delivers the advanced skills needed to architect performant LLMs that deliver real business impact. You'll learn how to make crucial architecture decisions, select optimal model types, configure hyperparameters, and curate quality training data. Discover professional techniques for pre-training, iterative fine-tuning, and rigorous model evaluation. The instructors reveal insider strategies to productionize your LLMs smoothly, monitor them proactively, and maintain optimal performance post-deployment. Following a structured curriculum spanning the complete LLM lifecycle, this masterclass empowers you with hands-on skills to build, refine, and deploy large language models with confidence. Turbocharge your generative AI initiatives and get the practical knowledge needed to create LLMs that solve complex challenges for your organization. What you will learn How to select the right LLM architecture for your use case Approaches for sourcing, cleaning, and labeling quality training data Pre-training methods and hyperparameter optimization Advanced fine-tuning techniques to boost performance Ways to thoroughly evaluate LLMs before deployment Best practices for monitoring, updating, and maintaining production LLMs Audience Data scientists, machine learning engineers, and AI developers seeking an in-depth understanding of large language model (LLM) development. This masterclass is designed for professionals aiming to build, optimize, and deploy enterprise-grade LLMs tailored to specific use cases, with a focus on practical skills and real business impact. About the Authors Maxime Labonne: Sr. Machine Learning Scientist, JPMorgan. Denis Rothman: Expert in AI Transformers including ChatGPT/GPT-4, Bestselling Author. Abi Aryan: ML Engineer and Author.
    Note: Online resource; title from title details screen (O'Reilly, viewed February 8, 2024)
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  • 9
    Online Resource
    Online Resource
    Transformers for natural language processing and computer vision : explore generative AI and large language models with Hugging Face, ChatGPT, GPT-4V, and DALL-E3 (2024)
    Birmingham, UK : Packt Publishing Ltd.
    Details
    ISBN: 9781805128724
    Language: English
    Pages: 1 online resource (728 pages) , illustrations
    Edition: Third edition.
    DDC: 006.3
    Keywords: ChatGPT ; Artificial intelligence Data processing ; Natural language processing (Computer science) ; Cloud computing ; Intelligence artificielle ; Informatique ; Traitement automatique des langues naturelles ; Infonuagique
    Abstract: Transformers for Natural Language Processing and Computer Vision, Third Edition, explores Large Language Model (LLM) architectures, applications, and various platforms (Hugging Face, OpenAI, and Google Vertex AI) used for Natural Language Processing (NLP) and Computer Vision (CV). The book guides you through different transformer architectures to the latest Foundation Models and Generative AI. You’ll pretrain and fine-tune LLMs and work through different use cases, from summarization to implementing question-answering systems with embedding-based search techniques. You will also learn the risks of LLMs, from hallucinations and memorization to privacy, and how to mitigate such risks using moderation models with rule and knowledge bases. You’ll implement Retrieval Augmented Generation (RAG) with LLMs to improve the accuracy of your models and gain greater control over LLM outputs. Dive into generative vision transformers and multimodal model architectures and build applications, such as image and video-to-text classifiers. Go further by combining different models and platforms and learning about AI agent replication. This book provides you with an understanding of transformer architectures, pretraining, fine-tuning, LLM use cases, and best practices.
    Note: Includes bibliographical references and index
    URL: lizenzpflichtig
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