Applied natural language processing in the enterprise / Arasanipalai Patel

Public ISBD Titre : Applied natural language processing in the enterprise : Teaching Machines to Read, Write, and Underst Type de document : texte imprimé Auteurs : Arasanipalai Patel, Auteur Editeur : Paris : O'Reilly Année de publication : 2021 Importance : 350 pages ISBN/ISSN/EAN : 978-1-4920-6257-8 Prix : 51745f Langues : Anglais (eng) Index. décimale : B.10.4 Ouvrages Universitaires et Généraux Résumé : This hands-on guide helps you get up to speed on the latest and most promising trends in NLP. With some Python experience and a basic understanding of machine learning, you'll learn how to build and deploy real-world NLP applications in your organization.NLP has exploded in popularity over the last few years. But while Google, Facebook, OpenAI, and others continue to release larger language models, many teams still struggle with building NLP applications that live up to the hype. This hands-on guide helps you get up to speed on the latest and most promising trends in NLP. With a basic understanding of machine learning and some Python experience, youall learn how to train and deploy real-world NLP applications in your organization. Authors Ankur Patel and Ajay Uppili Arasanipalai guide you through the process using code and examples that highlight the best practices in modern NLP. Use state-of-the-art NLP models such as BERT and GPT-3 to solve NLP tasks such as named entity recognition, text classification, semantic search, and reading comprehension Train NLP models with performance comparable or superior to that of out-of-the-box systems Learn about transformer architecture and modern tricks like transfer learning that have taken the NLP world by storm Become familiar with the tools of the trade, including spaCy, Hugging Face, and fast.ai Use Python and PyTorch to build core parts of the NLP pipeline from scratch, including tokenizers, embeddings, and language models Take your models out of Jupyter notebooks and learn how to deploy, monitor, and maintain them in productionAnkur A. Patel is the Co-Founder and Head of Data at Glean and the Co-Founder of Mellow. Glean uses NLP to extract data from invoices and generate vendor spend intelligence for clients. Mellow is on a mission to democratize NLP tasks such as entity resolution, named entity recognition, and text classification for everyone. Previously, Ankur led teams at 7Park Data, ThetaRay, and R-Squared Macro and began his career at Bridgewater Associates and J.P. Morgan. He is a graduate of Princeton University and lives in New York City. Ajay Arasanipalai is a deep learning researcher and student at University of Illinois at Urbana-Champaign. He's authored many popular articles that discuss state-of-the-art deep learning research. In March 2018, Ajay was invited to speak about accelerated deep learning at Think 2018, IBM's largest annual tech conference. Currently, as cochair of the ACM SIGAI chapter at the University of Illinois, he organizes educational workshops and projects for undergraduate students Applied natural language processing in the enterprise : Teaching Machines to Read, Write, and Underst [texte imprimé] / Arasanipalai Patel, Auteur . - Paris : O'Reilly, 2021 . - 350 pages. ISBN : 978-1-4920-6257-8 : 51745f Langues : Anglais (eng) Index. décimale : B.10.4 Ouvrages Universitaires et Généraux Résumé : This hands-on guide helps you get up to speed on the latest and most promising trends in NLP. With some Python experience and a basic understanding of machine learning, you'll learn how to build and deploy real-world NLP applications in your organization.NLP has exploded in popularity over the last few years. But while Google, Facebook, OpenAI, and others continue to release larger language models, many teams still struggle with building NLP applications that live up to the hype. This hands-on guide helps you get up to speed on the latest and most promising trends in NLP. With a basic understanding of machine learning and some Python experience, youall learn how to train and deploy real-world NLP applications in your organization. Authors Ankur Patel and Ajay Uppili Arasanipalai guide you through the process using code and examples that highlight the best practices in modern NLP. Use state-of-the-art NLP models such as BERT and GPT-3 to solve NLP tasks such as named entity recognition, text classification, semantic search, and reading comprehension Train NLP models with performance comparable or superior to that of out-of-the-box systems Learn about transformer architecture and modern tricks like transfer learning that have taken the NLP world by storm Become familiar with the tools of the trade, including spaCy, Hugging Face, and fast.ai Use Python and PyTorch to build core parts of the NLP pipeline from scratch, including tokenizers, embeddings, and language models Take your models out of Jupyter notebooks and learn how to deploy, monitor, and maintain them in productionAnkur A. Patel is the Co-Founder and Head of Data at Glean and the Co-Founder of Mellow. Glean uses NLP to extract data from invoices and generate vendor spend intelligence for clients. Mellow is on a mission to democratize NLP tasks such as entity resolution, named entity recognition, and text classification for everyone. Previously, Ankur led teams at 7Park Data, ThetaRay, and R-Squared Macro and began his career at Bridgewater Associates and J.P. Morgan. He is a graduate of Princeton University and lives in New York City. Ajay Arasanipalai is a deep learning researcher and student at University of Illinois at Urbana-Champaign. He's authored many popular articles that discuss state-of-the-art deep learning research. In March 2018, Ajay was invited to speak about accelerated deep learning at Think 2018, IBM's largest annual tech conference. Currently, as cochair of the ACM SIGAI chapter at the University of Illinois, he organizes educational workshops and projects for undergraduate students

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Au coeur du bitcoin / Andreas M. Antonopoulos

Public ISBD Titre : Au coeur du bitcoin : [programmer la blockchain] Type de document : texte imprimé Auteurs : Andreas M. Antonopoulos (1972-....), Auteur Editeur : Paris : O'Reilly Année de publication : DL 2019 Autre Editeur : 45-Malesherbes : Impr. EPAC technologies Importance : 1 vol. (XXXII-381 p.) Présentation : ill. Format : 23 cm ISBN/ISSN/EAN : 978-2-412-03745-4 Prix : 29, EUR Note générale : O'Reilly est aussi l'éditeur de la version originale Traduit de l'anglais En appendice, choix de documents Index Langues : Français (fre) Langues originales : Anglais (eng) Mots-clés : Bitcoin  Programmation  Blockchains Index. décimale : K.10 Web et Programmation Au coeur du bitcoin : [programmer la blockchain] [texte imprimé] / Andreas M. Antonopoulos (1972-....), Auteur . - Paris : O'Reilly : 45-Malesherbes : Impr. EPAC technologies, DL 2019 . - 1 vol. (XXXII-381 p.) : ill. ; 23 cm. ISBN : 978-2-412-03745-4 : 29, EUR O'Reilly est aussi l'éditeur de la version originale Traduit de l'anglais En appendice, choix de documents Index Langues : Français (fre) Langues originales : Anglais (eng) Mots-clés : Bitcoin  Programmation  Blockchains Index. décimale : K.10 Web et Programmation

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Generative Deep Learning / David Foster

Public ISBD Titre : Generative Deep Learning : Teaching Machines To Paint, Write, Compose, and Play Type de document : texte imprimé Auteurs : David Foster, Auteur Mention d'édition : 2ème édition Editeur : Paris : O'Reilly Année de publication : 2023 Importance : 453 pages ISBN/ISSN/EAN : 978-1-09-813418-1 Prix : 48100f Langues : Anglais (eng) Index. décimale : K.45 Intelligence artificielle et big-data, machine Learning Résumé : Generative AI is the hottest topic in tech. This practical book teaches machine learning engineers and data scientists how to use TensorFlow and Keras to create impressive generative deep learning models from scratch, including variational autoencoders (VAEs), generative adversarial networks (GANs), Transformers, normalizing flows, energy-based models, and denoising diffusion models. The book starts with the basics of deep learning and progresses to cutting-edge architectures. Through tips and tricks, you'll understand how to make your models learn more efficiently and become more creative. Generative Deep Learning : Teaching Machines To Paint, Write, Compose, and Play [texte imprimé] / David Foster, Auteur  . -  2ème édition . - Paris : O'Reilly, 2023 . - 453 pages. ISBN : 978-1-09-813418-1 : 48100f Langues : Anglais (eng) Index. décimale : K.45 Intelligence artificielle et big-data, machine Learning Résumé : Generative AI is the hottest topic in tech. This practical book teaches machine learning engineers and data scientists how to use TensorFlow and Keras to create impressive generative deep learning models from scratch, including variational autoencoders (VAEs), generative adversarial networks (GANs), Transformers, normalizing flows, energy-based models, and denoising diffusion models. The book starts with the basics of deep learning and progresses to cutting-edge architectures. Through tips and tricks, you'll understand how to make your models learn more efficiently and become more creative.

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PyTorch Pocket Reference / Joe Papa

Public ISBD Titre : PyTorch Pocket Reference : Building and Deploying Deep Learning Models Type de document : texte imprimé Auteurs : Joe Papa, Auteur Editeur : Paris : O'Reilly Année de publication : 2021 Importance : 293 pages ISBN/ISSN/EAN : 978-1-4920-9000-7 Prix : 16645f Langues : Anglais (eng) Index. décimale : K.45 Intelligence artificielle et big-data, machine Learning Résumé : This concise, easy-to-use reference puts one of the most popular frameworks for deep learning research and development at your fingertips. Author Joe Papa provides instant access to syntax, design patterns, and code examples to accelerate your development and reduce the time you spend searching for answers. Research scientists, machine learning engineers, and software developers will find clear, structured PyTorch code that covers every step of neural network development-from loading data to customizing training loops to model optimization and GPU/TPU acceleration. Quickly learn how to deploy your code to production using AWS, Google Cloud, or Azure and deploy your ML models to mobile and edge devices. Learn basic PyTorch syntax and design patterns Create custom models and data transforms Train and deploy models using a GPU and TPU Train and test a deep learning classifier Accelerate training using optimization and distributed training Access useful PyTorch libraries and the PyTorch ecosystem PyTorch Pocket Reference : Building and Deploying Deep Learning Models [texte imprimé] / Joe Papa, Auteur . - Paris : O'Reilly, 2021 . - 293 pages. ISBN : 978-1-4920-9000-7 : 16645f Langues : Anglais (eng) Index. décimale : K.45 Intelligence artificielle et big-data, machine Learning Résumé : This concise, easy-to-use reference puts one of the most popular frameworks for deep learning research and development at your fingertips. Author Joe Papa provides instant access to syntax, design patterns, and code examples to accelerate your development and reduce the time you spend searching for answers. Research scientists, machine learning engineers, and software developers will find clear, structured PyTorch code that covers every step of neural network development-from loading data to customizing training loops to model optimization and GPU/TPU acceleration. Quickly learn how to deploy your code to production using AWS, Google Cloud, or Azure and deploy your ML models to mobile and edge devices. Learn basic PyTorch syntax and design patterns Create custom models and data transforms Train and deploy models using a GPU and TPU Train and test a deep learning classifier Accelerate training using optimization and distributed training Access useful PyTorch libraries and the PyTorch ecosystem

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