Artificial intelligence by example / Denis Rothman (2020)

Public ISBD Titre : Artificial intelligence by example : acquire advanced AI, machine learning, and deep learning design skills Type de document : texte imprimé Auteurs : Denis Rothman, Auteur Mention d'édition : 2nd ed Editeur : Birmingham : Packt Publishing Année de publication : 2020 Importance : XXI, 549 p. Présentation : ill. Format : 24 cm ISBN/ISSN/EAN : 978-1-83921-153-9 Langues : Anglais (eng) Mots-clés : Artificial intelligence Machine learning Index. décimale : 004.8 Intelligence artificielle Résumé : 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. Note de contenu : Summary : 1. Getting started with next-generation artificial intelligence through reinforcement learning. 2. Building a reward matrix designing your datasets. 3. Machine intelligence evaluation functions and numerical convergence. 4. Optimizing your solutions with k-means clustering. 5. How to use decision trees to enhance k-means clustering. 6. Innovating ai with google translate. 7. Optimizing blockchains with naive bayes. 8. Solving the xor problem with a fnn. 9. Abstract image classification with cnn. 10. Conceptual representation learning. 11. Combining rl and dl. 12. Ai and the iot. 13. Visualizing networks with tensorflow 2.x and tensorboard. 14. Preparing the input of chatbots with rbms and pca. 15. Setting up a cognitive nlp ui/cui chatbot 16. Improving the emotional intelligence. deficiencies of chatbots. 17. Genetic algorithms in hybrid neural networks. 18. Neuromorphic computing. 19. Quantum computing Artificial intelligence by example : acquire advanced AI, machine learning, and deep learning design skills [texte imprimé] / Denis Rothman, Auteur  . -  2nd ed . - Birmingham : Packt Publishing, 2020 . - XXI, 549 p. : ill. ; 24 cm. ISBN : 978-1-83921-153-9 Langues : Anglais (eng) Mots-clés : Artificial intelligence Machine learning Index. décimale : 004.8 Intelligence artificielle Résumé : 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. Note de contenu : Summary : 1. Getting started with next-generation artificial intelligence through reinforcement learning. 2. Building a reward matrix designing your datasets. 3. Machine intelligence evaluation functions and numerical convergence. 4. Optimizing your solutions with k-means clustering. 5. How to use decision trees to enhance k-means clustering. 6. Innovating ai with google translate. 7. Optimizing blockchains with naive bayes. 8. Solving the xor problem with a fnn. 9. Abstract image classification with cnn. 10. Conceptual representation learning. 11. Combining rl and dl. 12. Ai and the iot. 13. Visualizing networks with tensorflow 2.x and tensorboard. 14. Preparing the input of chatbots with rbms and pca. 15. Setting up a cognitive nlp ui/cui chatbot 16. Improving the emotional intelligence. deficiencies of chatbots. 17. Genetic algorithms in hybrid neural networks. 18. Neuromorphic computing. 19. Quantum computing

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Artificial intelligence with Python / Alberto Artasanchez (2020)

Public ISBD Titre : Artificial intelligence with Python : your complete guide to building intelligent apps using Python 3.x Type de document : texte imprimé Auteurs : Alberto Artasanchez, Auteur ; Prateek Joshi, Auteur Mention d'édition : 2nd ed Editeur : Birmingham : Packt Publishing Année de publication : 2020 Importance : XVIII, 592 p. Présentation : ill. Format : 24 cm ISBN/ISSN/EAN : 978-1-83921-953-5 Note générale : Index Langues : Anglais (eng) Mots-clés : Python (Computer program language) Artificial intelligence -- Data processing Application software -- Development Python (langage de programmation) Intelligence artificielle Index. décimale : 004.43 Langage de programmation Résumé : Completely updated and revised edition of the bestselling guide to artificial intelligence, updated to Python 3.8 and TensorFlow 2, with seven new chapters that cover RNNs, AI & Big Data, fundamental use cases, machine learning data pipelines, chatbots, Big Data, and more Note de contenu : Summary : 1. Introduction to artificial intelligence. 2. Fundamental use cases for artificial intelligence. 3. Machine learning pipelines. 4. Feature selection and feature engineering. 5. Classification and regression using supervised learning. 6. Predictive analytics with ensemble learning. 7. Detecting patterns with unsupervised learning. 8. Building recommender systems. 9. Logic programming. 10. Heuristic search techniques. 11. Genetic algorithms and genetic programming. 12. Artificial intelligence on the cloud. 13. Building games with artificial intelligence. 14. Building a speech recognizer. 15. Natural language processing. 16. Chatbots. 17. Sequential data and time series analysis. 18. Image recognition. 19. Neural networks. 20. Deep learning with convolutional neural networks. 21. Recurrent neural networks and other deep learning models. 22. Creating intelligent agents with reinforcement learning. 23. Artificial intelligence and big data. Artificial intelligence with Python : your complete guide to building intelligent apps using Python 3.x [texte imprimé] / Alberto Artasanchez, Auteur ; Prateek Joshi, Auteur  . -  2nd ed . - Birmingham : Packt Publishing, 2020 . - XVIII, 592 p. : ill. ; 24 cm. ISBN : 978-1-83921-953-5 Index Langues : Anglais (eng) Mots-clés : Python (Computer program language) Artificial intelligence -- Data processing Application software -- Development Python (langage de programmation) Intelligence artificielle Index. décimale : 004.43 Langage de programmation Résumé : Completely updated and revised edition of the bestselling guide to artificial intelligence, updated to Python 3.8 and TensorFlow 2, with seven new chapters that cover RNNs, AI & Big Data, fundamental use cases, machine learning data pipelines, chatbots, Big Data, and more Note de contenu : Summary : 1. Introduction to artificial intelligence. 2. Fundamental use cases for artificial intelligence. 3. Machine learning pipelines. 4. Feature selection and feature engineering. 5. Classification and regression using supervised learning. 6. Predictive analytics with ensemble learning. 7. Detecting patterns with unsupervised learning. 8. Building recommender systems. 9. Logic programming. 10. Heuristic search techniques. 11. Genetic algorithms and genetic programming. 12. Artificial intelligence on the cloud. 13. Building games with artificial intelligence. 14. Building a speech recognizer. 15. Natural language processing. 16. Chatbots. 17. Sequential data and time series analysis. 18. Image recognition. 19. Neural networks. 20. Deep learning with convolutional neural networks. 21. Recurrent neural networks and other deep learning models. 22. Creating intelligent agents with reinforcement learning. 23. Artificial intelligence and big data.

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Database design and modeling with google cloud (2023)

Public ISBD Titre : Database design and modeling with google cloud : learn database design and development to take your data to applications, analytics, and AI Type de document : document électronique Editeur : Birmingham : Packt Publishing Année de publication : 2023 Importance : 1 fichier PDF Présentation : ill. ISBN/ISSN/EAN : 978-1-80461-786-1 Note générale : Mode d'accès : accès au texte intégral par : - authentification après inscription à la plateforme EBSCOhost ou - adresse IP de l'École. Index Mots-clés : Databases Databese design Cloud computing Artificial intelligence Index. décimale : 004.65 Bases de données et leurs structures Résumé : Build faster and efficient real-world applications on the cloud with a fitting database model that's perfect for your needsKey FeaturesFamiliarize yourself with business and technical considerations involved in modeling the right databaseTake your data to applications, analytics, and AI with real-world examplesLearn how to code, build, and deploy end-to-end solutions with expert advicePurchase of the print or Kindle book includes a free PDF eBookBook DescriptionIn the age of lightning-speed delivery, customers want everything developed, built, and delivered at high speed and at scale. Knowledge, design, and choice of database is critical in that journey, but there is no one-size-fits-all solution. This book serves as a comprehensive and practical guide for data professionals who want to design and model their databases efficiently. The book begins by taking you through business, technical, and design considerations for databases. Next, it takes you on an immersive structured database deep dive for both transactional and analytical real-world use cases using Cloud SQL, Spanner, and BigQuery. As you progress, you'll explore semi-structured and unstructured database considerations with practical applications using Firestore, cloud storage, and more. You'll also find insights into operational considerations for databases and the database design journey for taking your data to AI with Vertex AI APIs and generative AI examples. By the end of this book, you will be well-versed in designing and modeling data and databases for your applications using Google Cloud.What you will learnUnderstand different use cases and real-world applications of data in the cloudWork with document and indexed NoSQL databasesGet to grips with modeling considerations for analytics, AI, and MLUse real-world examples to learn about ETL servicesDesign structured, semi-structured, and unstructured data for your applications and analyticsImprove observability, performance, security, scalability, latency SLAs, SLIs, and SLOsWho this book is forThis book is for database developers, data engineers, and architects looking to design, model, and build database applications on the cloud with an extended focus on operational consideration and taking their data to AI. Data scientists, as well ML and AI engineers who want to use Google Cloud services in the data to AI journey will also find plenty of useful information in this book. It will also be useful to data analysts and BI developers who want to use SQL impactfully to generate ML and generative AI insights from their data. Note de contenu : Summary : I. Database model: business and technical design considerations. 1. Data, databases, and design. 2. Handling data on the cloud. II. Structured data. 3. Database modeling for structured data. 4. Setting up a fully managed RDBMS. 5. Designing an analytical data warehouse. III. Semi-structured, unstructured data, and NoSQL design. 6. Designing for semi-structured data. 7. Unstructured data management. IV. Devops and databases. 8. Devops and databases. V. Data to AI. 9. Data to AI – modeling your databases for analytics and ML. 10. Looking ahead – designing for LLM applications. Database design and modeling with google cloud : learn database design and development to take your data to applications, analytics, and AI [document électronique] . - Birmingham : Packt Publishing, 2023 . - 1 fichier PDF : ill. ISBN : 978-1-80461-786-1 Mode d'accès : accès au texte intégral par : - authentification après inscription à la plateforme EBSCOhost ou - adresse IP de l'École. Index Mots-clés : Databases Databese design Cloud computing Artificial intelligence Index. décimale : 004.65 Bases de données et leurs structures Résumé : Build faster and efficient real-world applications on the cloud with a fitting database model that's perfect for your needsKey FeaturesFamiliarize yourself with business and technical considerations involved in modeling the right databaseTake your data to applications, analytics, and AI with real-world examplesLearn how to code, build, and deploy end-to-end solutions with expert advicePurchase of the print or Kindle book includes a free PDF eBookBook DescriptionIn the age of lightning-speed delivery, customers want everything developed, built, and delivered at high speed and at scale. Knowledge, design, and choice of database is critical in that journey, but there is no one-size-fits-all solution. This book serves as a comprehensive and practical guide for data professionals who want to design and model their databases efficiently. The book begins by taking you through business, technical, and design considerations for databases. Next, it takes you on an immersive structured database deep dive for both transactional and analytical real-world use cases using Cloud SQL, Spanner, and BigQuery. As you progress, you'll explore semi-structured and unstructured database considerations with practical applications using Firestore, cloud storage, and more. You'll also find insights into operational considerations for databases and the database design journey for taking your data to AI with Vertex AI APIs and generative AI examples. By the end of this book, you will be well-versed in designing and modeling data and databases for your applications using Google Cloud.What you will learnUnderstand different use cases and real-world applications of data in the cloudWork with document and indexed NoSQL databasesGet to grips with modeling considerations for analytics, AI, and MLUse real-world examples to learn about ETL servicesDesign structured, semi-structured, and unstructured data for your applications and analyticsImprove observability, performance, security, scalability, latency SLAs, SLIs, and SLOsWho this book is forThis book is for database developers, data engineers, and architects looking to design, model, and build database applications on the cloud with an extended focus on operational consideration and taking their data to AI. Data scientists, as well ML and AI engineers who want to use Google Cloud services in the data to AI journey will also find plenty of useful information in this book. It will also be useful to data analysts and BI developers who want to use SQL impactfully to generate ML and generative AI insights from their data. Note de contenu : Summary : I. Database model: business and technical design considerations. 1. Data, databases, and design. 2. Handling data on the cloud. II. Structured data. 3. Database modeling for structured data. 4. Setting up a fully managed RDBMS. 5. Designing an analytical data warehouse. III. Semi-structured, unstructured data, and NoSQL design. 6. Designing for semi-structured data. 7. Unstructured data management. IV. Devops and databases. 8. Devops and databases. V. Data to AI. 9. Data to AI – modeling your databases for analytics and ML. 10. Looking ahead – designing for LLM applications.

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Deep learning with TensorFlow 2 and Keras / Antonio Gulli (2019)

Public ISBD Titre : Deep learning with TensorFlow 2 and Keras : regression, ConvNets, GANs, RNNs, NLP, and more with TensorFlow 2 and the Keras API Type de document : texte imprimé Auteurs : Antonio Gulli, Auteur ; Amita Kapoor, Auteur ; Sujit Pal, Auteur Mention d'édition : 2nd ed Editeur : Birmingham : Packt Publishing Année de publication : 2019 Importance : XXV, 610 p. Présentation : ill. Format : 24 cm ISBN/ISSN/EAN : 978-1-83882-341-2 Note générale : Bibliogr. en fin de chapitres. Index Langues : Anglais (eng) Mots-clés : TensorFlow Machine learning Natural language processing Application program interfaces Python Neural networks Index. décimale : 004.8 Intelligence artificielle Résumé : Deep Learning with TensorFlow 2 and Keras, Second Edition teaches neural networks and deep learning techniques alongside TensorFlow (TF) and Keras. You'll learn how to write deep learning applications in the most powerful, popular, and scalable machine learning stack available. TensorFlow is the machine learning library of choice for professional applications, while Keras offers a simple and powerful Python API for accessing TensorFlow. TensorFlow 2 provides full Keras integration, making advanced machine learning easier and more convenient than ever before. This book also introduces neural networks with TensorFlow, runs through the main applications (regression, ConvNets (CNNs), GANs, RNNs, NLP), covers two working example apps, and then dives into TF in production, TF mobile, and using TensorFlow with AutoML. Note de contenu : Summary : 1. Neural network foundations with TensorFlow 2.0. 2. Tensorflow 1.x and 2.x. 3. Regression. 4. Convolutional neural networks. 5. Advanced convolutional neural networks. 6. Generative adversarial networks. 7. Word embeddings. 8. Recurrent neural networks. 9. Autoencoders. 10.Unsupervised learning. 11.Reinforcement learning. 12. TensorFlow and Cloud. 13. TensorFlow for mobile and IoT and TensorFlow.js. 14. An introduction to automl. 15. The math behind deep learning. 16. Tensor processing unit. Deep learning with TensorFlow 2 and Keras : regression, ConvNets, GANs, RNNs, NLP, and more with TensorFlow 2 and the Keras API [texte imprimé] / Antonio Gulli, Auteur ; Amita Kapoor, Auteur ; Sujit Pal, Auteur  . -  2nd ed . - Birmingham : Packt Publishing, 2019 . - XXV, 610 p. : ill. ; 24 cm. ISBN : 978-1-83882-341-2 Bibliogr. en fin de chapitres. Index Langues : Anglais (eng) Mots-clés : TensorFlow Machine learning Natural language processing Application program interfaces Python Neural networks Index. décimale : 004.8 Intelligence artificielle Résumé : Deep Learning with TensorFlow 2 and Keras, Second Edition teaches neural networks and deep learning techniques alongside TensorFlow (TF) and Keras. You'll learn how to write deep learning applications in the most powerful, popular, and scalable machine learning stack available. TensorFlow is the machine learning library of choice for professional applications, while Keras offers a simple and powerful Python API for accessing TensorFlow. TensorFlow 2 provides full Keras integration, making advanced machine learning easier and more convenient than ever before. This book also introduces neural networks with TensorFlow, runs through the main applications (regression, ConvNets (CNNs), GANs, RNNs, NLP), covers two working example apps, and then dives into TF in production, TF mobile, and using TensorFlow with AutoML. Note de contenu : Summary : 1. Neural network foundations with TensorFlow 2.0. 2. Tensorflow 1.x and 2.x. 3. Regression. 4. Convolutional neural networks. 5. Advanced convolutional neural networks. 6. Generative adversarial networks. 7. Word embeddings. 8. Recurrent neural networks. 9. Autoencoders. 10.Unsupervised learning. 11.Reinforcement learning. 12. TensorFlow and Cloud. 13. TensorFlow for mobile and IoT and TensorFlow.js. 14. An introduction to automl. 15. The math behind deep learning. 16. Tensor processing unit.

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Intelligent automation with IBM cloud pak for business automation (2022)

Public ISBD Titre : Intelligent automation with IBM cloud pak for business automation : a practical guide to automating enterprise business workflows to deliver intelligent solutions Type de document : document électronique Editeur : Birmingham : Packt Publishing Année de publication : 2022 Importance : 1 fichier PDF Présentation : ill. ISBN/ISSN/EAN : 978-1-80181-112-5 Note générale : Mode d'accès : accès au texte intégral par : - authentification après inscription à la plateforme EBSCOhost ou - adresse IP de l'École Index Mots-clés : Business--Data processing Big data Cloud computing Information technology--Management Résumé : Leverage the low-code/no-code approach in IBM Cloud Pak for business automation to accelerate your organization's digital transformation Purchase of the print or Kindle book includes a free eBook PDFKey FeaturesGet a comprehensive understanding of IBM Cloud Pak for Business AutomationTake a deep dive into insights on RPA, workflow automation, and automated decisionsDeploy and manage production-grade automated solutions for scalability, stability, and performanceBook DescriptionCOVID-19 has made many businesses change how they work, change how they engage their customers, and even change their products. Several of these businesses have also recognized the need to make these changes within days as opposed to months or weeks. This has resulted in an unprecedented pace of digital transformation; and success, in many cases, depends on how quickly an organization can react to real-time decisions. This book begins by introducing you to IBM Cloud Pak for Business Automation, providing a hands-on approach to project implementation. As you progress through the chapters, you'll learn to take on business problems and identify the relevant technology and starting point. Next, you'll find out how to engage both the business and IT community to better understand business problems, as well as explore practical ways to start implementing your first automation project. In addition, the book will show you how to create task automation, interactive chatbots, workflow automation, and document processing. Finally, you'll discover deployment best practices that'll help you support highly available and resilient solutions. By the end of this book, you'll have a firm grasp on the types of business problems that can be solved with IBM Cloud Pak for Business Automation.What you will learnUnderstand key IBM automation technologies and learn how to apply themCover the end-to-end journey of creating an automation solution from concept to deploymentUnderstand the features and capabilities of workflow, decisions, RPA, business applications, and document processing with AIAnalyze your business processes and discover automation opportunities with process miningSet up content management solutions that meet business, regulatory, and compliance needsUnderstand deployment environments supported by IBM Cloud Pak for Business AutomationWho this book is forThis book is for robotic process automation (RPA) professionals and automation consultants who want to accelerate the digital transformation of their businesses using IBM automation. This book is also useful for solutions architects or enterprise architects looking for best practices to build resilient and scalable AI-driven automation solutions. A basic understanding of business processes, low-code visual modeling techniques, RPA, and AI concepts is assumed. Note de contenu : Summary : I. Business Automation and Cloud Pak Overview. 1. What Is Cloud Pak for Business Automation? 2. RPA, Workflow, Decisions, and Business Applications. 3. Process Discovery and Process Mining. ... II. Use Cases and Best Practices. 5. Task Automation with RPA. 6. Chatbot with RPA. 7. Workflow for Process Automation. ... III. Deployment Considerations. 13. On-Premises and On-Cloud Deployments. 14. Deployment Topologies, High Availability, and Disaster Recovery. 15. Automating Your Operations and Other Considerations. Intelligent automation with IBM cloud pak for business automation : a practical guide to automating enterprise business workflows to deliver intelligent solutions [document électronique] . - Birmingham : Packt Publishing, 2022 . - 1 fichier PDF : ill. ISBN : 978-1-80181-112-5 Mode d'accès : accès au texte intégral par : - authentification après inscription à la plateforme EBSCOhost ou - adresse IP de l'École Index Mots-clés : Business--Data processing Big data Cloud computing Information technology--Management Résumé : Leverage the low-code/no-code approach in IBM Cloud Pak for business automation to accelerate your organization's digital transformation Purchase of the print or Kindle book includes a free eBook PDFKey FeaturesGet a comprehensive understanding of IBM Cloud Pak for Business AutomationTake a deep dive into insights on RPA, workflow automation, and automated decisionsDeploy and manage production-grade automated solutions for scalability, stability, and performanceBook DescriptionCOVID-19 has made many businesses change how they work, change how they engage their customers, and even change their products. Several of these businesses have also recognized the need to make these changes within days as opposed to months or weeks. This has resulted in an unprecedented pace of digital transformation; and success, in many cases, depends on how quickly an organization can react to real-time decisions. This book begins by introducing you to IBM Cloud Pak for Business Automation, providing a hands-on approach to project implementation. As you progress through the chapters, you'll learn to take on business problems and identify the relevant technology and starting point. Next, you'll find out how to engage both the business and IT community to better understand business problems, as well as explore practical ways to start implementing your first automation project. In addition, the book will show you how to create task automation, interactive chatbots, workflow automation, and document processing. Finally, you'll discover deployment best practices that'll help you support highly available and resilient solutions. By the end of this book, you'll have a firm grasp on the types of business problems that can be solved with IBM Cloud Pak for Business Automation.What you will learnUnderstand key IBM automation technologies and learn how to apply themCover the end-to-end journey of creating an automation solution from concept to deploymentUnderstand the features and capabilities of workflow, decisions, RPA, business applications, and document processing with AIAnalyze your business processes and discover automation opportunities with process miningSet up content management solutions that meet business, regulatory, and compliance needsUnderstand deployment environments supported by IBM Cloud Pak for Business AutomationWho this book is forThis book is for robotic process automation (RPA) professionals and automation consultants who want to accelerate the digital transformation of their businesses using IBM automation. This book is also useful for solutions architects or enterprise architects looking for best practices to build resilient and scalable AI-driven automation solutions. A basic understanding of business processes, low-code visual modeling techniques, RPA, and AI concepts is assumed. Note de contenu : Summary : I. Business Automation and Cloud Pak Overview. 1. What Is Cloud Pak for Business Automation? 2. RPA, Workflow, Decisions, and Business Applications. 3. Process Discovery and Process Mining. ... II. Use Cases and Best Practices. 5. Task Automation with RPA. 6. Chatbot with RPA. 7. Workflow for Process Automation. ... III. Deployment Considerations. 13. On-Premises and On-Cloud Deployments. 14. Deployment Topologies, High Availability, and Disaster Recovery. 15. Automating Your Operations and Other Considerations.

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MATLAB for machine learning (2024)

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Python machine learning / Sebastian Raschka (2017)

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Python machine learning / Sebastian Raschka (2015) 

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Python natural language processing / Jalaj Thanaki (2017) 

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