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Auteur Denis ROTHMAN |
Documents disponibles écrits par cet auteur (4)
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Titre : Transformers for Natural Language Processing Type de document : e-book Auteurs : Denis ROTHMAN Editeur : PACKT PUBLISHING Année de publication : 2022 ISBN/ISSN/EAN : 9781803247335 Note générale : copyrighted Langues : Anglais (eng) Résumé : Under the hood working of transformers, fine-tuning GPT-3 models, DeBERTa, vision models, and the start of Metaverse, using a variety of NLP platforms: Hugging Face, OpenAI API, Trax, and AllenNLPKey FeaturesImplement models, such as BERT, Reformer, and T5, that outperform classical language modelsCompare NLP applications using GPT-3, GPT-2, and other transformersAnalyze advanced use cases, including polysemy, cross-lingual learning, and computer visionBook DescriptionTransformers 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.What you will learnDiscover new ways of performing NLP techniques with the latest pretrained transformersGrasp the workings of the original Transformer, GPT-3, BERT, T5, DeBERTa, and ReformerFind out how ViT and CLIP label images (including blurry ones!) and reconstruct images using DALL-ECarry out sentiment analysis, text summarization, casual language analysis, machine translations, and more using TensorFlow, PyTorch, and GPT-3Measure the productivity of key transformers to define their scope, potential, and limits in productionWho this book is forIf you want to learn about and apply transformers to your natural language (and image) data, this book is for you.A good understanding of NLP, Python, and deep learning is required to benefit most from this book. Many platforms covered in this book provide interactive user interfaces, which allow readers with a general interest in NLP and AI to follow several chapters of this book. Nombre d'accès : Illimité En ligne : http://library.ez.neoma-bs.fr/login?url=https://www.scholarvox.com/book/88929010 Permalink : https://cataloguelibrary.neoma-bs.fr/index.php?lvl=notice_display&id=544992
Titre : Artificial Intelligence By Example Type de document : e-book Auteurs : Denis ROTHMAN Editeur : PACKT PUBLISHING Année de publication : 2020 ISBN/ISSN/EAN : 9781839211539 Note générale : copyrighted Langues : Anglais (eng) Résumé : 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. What you will learn 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 them practically. Prior experience with Python programming and statistical knowledge is essential to make the most out of this book. Nombre d'accès : Illimité En ligne : http://library.ez.neoma-bs.fr/login?url=https://www.scholarvox.com/book/88882290 Permalink : https://cataloguelibrary.neoma-bs.fr/index.php?lvl=notice_display&id=504352
Titre : Hands-On Explainable AI (XAI) with Python Type de document : e-book Auteurs : Denis ROTHMAN Editeur : PACKT PUBLISHING Année de publication : 2020 ISBN/ISSN/EAN : 9781800208131 Note générale : copyrighted Langues : Anglais (eng) Résumé : 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. You must have some foundational knowledge and/or experience with machine learning libraries such as scikit-learn to make the most out of this book. Some of the potential readers of this book include: Professionals who already use Python for as data science, machine learning, research, and analysis Data analysts and data scientists who want an introduction into explainable AI tools and techniques AI Project managers who must face the contractual and legal obligations of AI Explainability for the acceptance phase of their applications Nombre d'accès : Illimité En ligne : http://library.ez.neoma-bs.fr/login?url=https://www.scholarvox.com/book/88900549 Permalink : https://cataloguelibrary.neoma-bs.fr/index.php?lvl=notice_display&id=516369
Titre : Artificial Intelligence By Example Type de document : e-book Auteurs : Denis ROTHMAN Editeur : PACKT PUBLISHING Année de publication : 2018 ISBN/ISSN/EAN : 9781788990547 Note générale : copyrighted Langues : Anglais (eng) Nombre d'accès : Illimité En ligne : http://library.ez.neoma-bs.fr/login?url=https://www.scholarvox.com/book/88856936 Permalink : https://cataloguelibrary.neoma-bs.fr/index.php?lvl=notice_display&id=481296
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