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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 learningIndex. 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 computingArtificial 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 learningIndex. 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 computingRéservation
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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 artificielleIndex. 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 artificielleIndex. 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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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 networksIndex. 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 networksIndex. 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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Titre : Python machine learning : machine learning and deep learning with Python, scikit-learn, and tensorflow Type de document : texte imprimé Auteurs : Sebastian Raschka, Auteur ; Vahid Mirjalili, Auteur Mention d'édition : 2nd ed Editeur : Birmingham : Packt Publishing Année de publication : 2017 Importance : XVIII, 594 p. Présentation : ill. Format : 24 cm ISBN/ISSN/EAN : 978-1-78712-593-3 Note générale : Index Langues : Anglais (eng) Mots-clés : Python (langage de programmation)
Apprentissage automatique
Apprentissage profond
Python (Computer program language)
Machine learningIndex. décimale : 004.43 Langage de programmation Résumé : Machine learning is eating the software world, and now deep learning is extending machine learning. Understand and work at the cutting edge of machine learning, neural networks, and deep learning with this second edition of Sebastian Raschka's bestselling book, Python Machine Learning. Using Python's open source libraries, this book offers the practical knowledge and techniques you need to create and contribute to machine learning, deep learning, and modern data analysis.
Fully extended and modernized, Python Machine Learning Second Edition now includes the popular TensorFlow 1.x deep learning library. The scikit-learn code has also been fully updated to v0.18.1 to include improvements and additions to this versatile machine learning library.Note de contenu : Summary :
1. Giving computers the ability to learn from data.
2. Training simple machine learning algorithms fo classification.
3. A tour of machine learning classifiers using scikit-learn.
4. Building good trainnig sets - data preprocessing.
5. Compressing data via dimensionality reduction.
6. Learning best practices for model evaluation and hyperparameter tuning.
...Python machine learning : machine learning and deep learning with Python, scikit-learn, and tensorflow [texte imprimé] / Sebastian Raschka, Auteur ; Vahid Mirjalili, Auteur . - 2nd ed . - Birmingham : Packt Publishing, 2017 . - XVIII, 594 p. : ill. ; 24 cm.
ISBN : 978-1-78712-593-3
Index
Langues : Anglais (eng)
Mots-clés : Python (langage de programmation)
Apprentissage automatique
Apprentissage profond
Python (Computer program language)
Machine learningIndex. décimale : 004.43 Langage de programmation Résumé : Machine learning is eating the software world, and now deep learning is extending machine learning. Understand and work at the cutting edge of machine learning, neural networks, and deep learning with this second edition of Sebastian Raschka's bestselling book, Python Machine Learning. Using Python's open source libraries, this book offers the practical knowledge and techniques you need to create and contribute to machine learning, deep learning, and modern data analysis.
Fully extended and modernized, Python Machine Learning Second Edition now includes the popular TensorFlow 1.x deep learning library. The scikit-learn code has also been fully updated to v0.18.1 to include improvements and additions to this versatile machine learning library.Note de contenu : Summary :
1. Giving computers the ability to learn from data.
2. Training simple machine learning algorithms fo classification.
3. A tour of machine learning classifiers using scikit-learn.
4. Building good trainnig sets - data preprocessing.
5. Compressing data via dimensionality reduction.
6. Learning best practices for model evaluation and hyperparameter tuning.
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Titre : Python machine learning : unlock deeper insights into machine learning with this vital guide to cutting-edge predictive analytics Type de document : document électronique Auteurs : Sebastian Raschka, Auteur Editeur : Birmingham : Packt Publishing Année de publication : 2015 Importance : 1 fichier PDF (33 Mo) Présentation : ill. ISBN/ISSN/EAN : 978-1-78355-513-0 Note générale : Mode d'accès : accès au texte intégral par intranet. Langues : Anglais (eng) Mots-clés : Python (Computer program language)
Machine learning
Python (langage de programmation)
Apprentissage machineIndex. décimale : 004.4 Logiciel. Programme Résumé : Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analyticsAbout This Book- Leverage Python's most powerful open-source libraries for deep learning, data wrangling, and data visualization- Learn effective strategies and best practices to improve and optimize machine learning systems and algorithms- Ask - and answer - tough questions of your data with robust statistical models, built for a range of datasetsWho This Book Is ForIf you want to find out how to use Python to start answering critical questions of your data, pick up Python Machine Learning - whether you want to get started from scratch or want to extend your data science knowledge, this is an essential and unmissable resource.What You Will Learn- Explore how to use different machine learning models to ask different questions of your data- Learn how to build neural networks using Keras and Theano- Find out how to write clean and elegant Python code that will optimize the strength of your algorithms- Discover how to embed your machine learning model in a web application for increased accessibility- Predict continuous target outcomes using regression analysis- Uncover hidden patterns and structures in data with clustering- Organize data using effective pre-processing techniques- Get to grips with sentiment analysis to delve deeper into textual and social media dataIn DetailMachine learning and predictive analytics are transforming the way businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data - its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success.Python Machine Learning gives you access to the world of predictive analytics and demonstrates why Python is one of the world's leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science book is invaluable. Covering a wide range of powerful Python libraries, including scikit-learn, Theano, and Keras, and featuring guidance and tips on everything from sentiment analysis to neural networks, you'll soon be able to answer some of the most important questions facing you and your organization.Style and approachPython Machine Learning connects the fundamental theoretical principles behind machine learning to their practical application in a way that focuses you on asking and answering the right questions. It walks you through the key elements of Python and its powerful machine learning libraries, while demonstrating how to get to grips with a range of statistical models. Python machine learning : unlock deeper insights into machine learning with this vital guide to cutting-edge predictive analytics [document électronique] / Sebastian Raschka, Auteur . - Birmingham : Packt Publishing, 2015 . - 1 fichier PDF (33 Mo) : ill.
ISBN : 978-1-78355-513-0
Mode d'accès : accès au texte intégral par intranet.
Langues : Anglais (eng)
Mots-clés : Python (Computer program language)
Machine learning
Python (langage de programmation)
Apprentissage machineIndex. décimale : 004.4 Logiciel. Programme Résumé : Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analyticsAbout This Book- Leverage Python's most powerful open-source libraries for deep learning, data wrangling, and data visualization- Learn effective strategies and best practices to improve and optimize machine learning systems and algorithms- Ask - and answer - tough questions of your data with robust statistical models, built for a range of datasetsWho This Book Is ForIf you want to find out how to use Python to start answering critical questions of your data, pick up Python Machine Learning - whether you want to get started from scratch or want to extend your data science knowledge, this is an essential and unmissable resource.What You Will Learn- Explore how to use different machine learning models to ask different questions of your data- Learn how to build neural networks using Keras and Theano- Find out how to write clean and elegant Python code that will optimize the strength of your algorithms- Discover how to embed your machine learning model in a web application for increased accessibility- Predict continuous target outcomes using regression analysis- Uncover hidden patterns and structures in data with clustering- Organize data using effective pre-processing techniques- Get to grips with sentiment analysis to delve deeper into textual and social media dataIn DetailMachine learning and predictive analytics are transforming the way businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data - its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success.Python Machine Learning gives you access to the world of predictive analytics and demonstrates why Python is one of the world's leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science book is invaluable. Covering a wide range of powerful Python libraries, including scikit-learn, Theano, and Keras, and featuring guidance and tips on everything from sentiment analysis to neural networks, you'll soon be able to answer some of the most important questions facing you and your organization.Style and approachPython Machine Learning connects the fundamental theoretical principles behind machine learning to their practical application in a way that focuses you on asking and answering the right questions. It walks you through the key elements of Python and its powerful machine learning libraries, while demonstrating how to get to grips with a range of statistical models. Réservation
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