Download or read book Apprentissage artificiel 4e dition written by Vincent Barra and published by Editions Eyrolles. This book was released on 2021-04-01 with total page 1004 pages. Available in PDF, EPUB and Kindle. Book excerpt: Les programmes d'intelligence artificielle sont aujourd'hui capables de reconnaître des commandes vocales, d'analyser automatiquement des photos satellites, d'assister des experts pour prendre des décisions dans des environnements complexes et évolut

Download or read book Apprentissage artificiel 3e dition written by Vincent Barra and published by Editions Eyrolles. This book was released on 2018-05-17 with total page 914 pages. Available in PDF, EPUB and Kindle. Book excerpt: Résumé Les programmes d'intelligence artificielle sont aujourd'hui capables de reconnaître des commandes vocales, d'analyser automatiquement des photos satellites, d'assister des experts pour prendre des décisions dans des environnements complexes et évolutifs (analyse de marchés financiers, diagnostics médicaux...), de fouiller d'immenses bases de données hétérogènes, telles les innombrables pages du Web... Pour réaliser ces tâches, ils sont dotés de modules d'apprentissage leur permettant d'adapter leur comportement à des situations jamais rencontrées, ou d'extraire des lois à partir de bases de données d'exemples. Ce livre présente les concepts qui sous-tendent l'apprentissage artificiel, les algorithmes qui en découlent et certaines de leurs applications. Son objectif est de décrire un ensemble d'algorithmes utiles en tentant d'établir un cadre théorique pour l'ensemble des techniques regroupées sous ce terme "d'apprentissage artificiel". La troisième édition de ce livre a été complètement réorganisée pour s'adapter aux évolutions très significatives de l'apprentissage artificiel ces dernières années. Une large place y est accordée aux techniques d'apprentissage profond et à de nouvelles applications, incluant le traitement de flux de données. À qui s'adresse ce livre ? Ce livre s'adresse tant aux décideurs et aux ingénieurs qui souhaitent mettre au point des applications qu'aux étudiants de niveau Master 1 et 2 et en école d'ingénieurs, qui souhaitent un ouvrage de référence sur ce domaine clé de l'intelligence artificielle.

Download or read book Apprentissage artificiel written by Vincent Barra and published by Editions Eyrolles. This book was released on 2021 with total page 1004 pages. Available in PDF, EPUB and Kindle. Book excerpt: Les programmes d'intelligence artificielle sont aujourd'hui capables de reconnaître des commandes vocales, d'analyser automatiquement des photos satellites, d'assister des experts pour prendre des décisions dans des environnements complexes et évolutifs (analyse de marchés financiers, diagnostics médicaux...), de fouiller d'immenses bases de données hétérogènes, telles les innombrables pages du Web... Pour réaliser ces tâches, ils sont dotés de modules d'apprentissage leur permettant d'adapter leur comportement à des situations jamais rencontrées, ou d'extraire des lois à partir de bases de données d'exemples. Ce livre présente les concepts qui sous-tendent l'apprentissage artificiel, les algorithmes qui en découlent et certaines de leurs applications. Son objectif est de décrire un ensemble d'algorithmes utiles en tentant d'établir un cadre théorique pour l'ensemble des techniques regroupées sous ce terme « d'apprentissage artificiel ». La quatrième édition de ce livre a été augmentée et complètement réorganisée pour s'adapter aux évolutions très significatives de l'apprentissage artificiel ces dernières années. Une large place y est accordée aux techniques d'apprentissage profond et à de nouvelles applications, incluant le traitement de flux de données. À qui s'adresse ce livre ? Ce livre s'adresse tant aux décideurs et aux ingénieurs qui souhaitent mettre au point des applications qu'aux étudiants de niveau Master 1 et 2 et en école d'ingénieurs, qui souhaitent un ouvrage de référence sur ce domaine clé de l'intelligence artificielle

Download or read book Apprentissage artificiel written by Antoine Cornuéjols and published by . This book was released on 2018 with total page 914 pages. Available in PDF, EPUB and Kindle. Book excerpt: Les programmes d'intelligence artificielle sont aujourd'hui capables de reconnaître des commandes vocales, d'analyser automatiquement des photos satellites, d'assister des experts pour prendre des décisions dans des environnements complexes et évolutifs (analyse de marchés financiers, diagnostics médicaux...), de fouiller d'immenses bases de données hétérogènes, telles les innombrables pages du Web... Pour réaliser ces tâches, ils sont dotés de modules d'apprentissage leur permettant d'adapter leur comportement à des situations jamais rencontrées, ou d'extraire des lois à partir de bases de données d'exemples. Ce livre présente les concepts qui sous-tendent l'apprentissage artificiel, les algorithmes qui en découlent et certaines de leurs applications. Son objectif est de décrire un ensemble d'algorithmes utiles en tentant d'établir un cadre théorique pour l'ensemble des techniques regroupées sous ce terme "d'apprentissage artificiel". La troisième édition de ce livre a été complètement réorganisée pour s'adapter aux évolutions très significatives de l'apprentissage artificiel ces dernières années. Une large place y est accordée aux techniques d'apprentissage profond et à de nouvelles applications, incluant le traitement de flux de données. [Cit. 4e de couv.]

Download or read book Digital Image Processing with C written by David Tschumperle and published by CRC Press. This book was released on 2023-03-23 with total page 309 pages. Available in PDF, EPUB and Kindle. Book excerpt: Digital Image Processing with C++ presents the theory of digital image processing, and implementations of algorithms using a dedicated library. Processing a digital image means transforming its content (denoising, stylizing, etc.), or extracting information to solve a given problem (object recognition, measurement, motion estimation, etc.). This book presents the mathematical theories underlying digital image processing, as well as their practical implementation through examples of algorithms implemented in the C++ language, using the free and easy-to-use CImg library. Chapters cover in a broad way the field of digital image processing and proposes practical and functional implementations of each method theoretically described. The main topics covered include filtering in spatial and frequency domains, mathematical morphology, feature extraction and applications to segmentation, motion estimation, multispectral image processing and 3D visualization. Students or developers wishing to discover or specialize in this discipline, teachers and researchers wishing to quickly prototype new algorithms, or develop courses, will all find in this book material to discover image processing or deepen their knowledge in this field.

Download or read book Apprentissage artificiel written by Antoine Cornuéjols and published by . This book was released on 2002 with total page 591 pages. Available in PDF, EPUB and Kindle. Book excerpt: Les programmes d'intelligence artificielle sont aujourd'hui capables de reconnaître des commandes vocales, d'analyser automatiquement des photos satellites, d'assister des experts pour prendre des décisions dans des environnements complexes et évolutifs (analyse de marchés financiers, diagnostics médicaux...), de fouiller d'immenses bases de données hétérogènes, telles les innombrables pages du Web... Pour réaliser ces tâches, ils sont dotés de modules d'apprentissage leur permettant d'adapter leur comportement à des situations jamais rencontrées, ou d'extraire des lois à partir de bases de données d'exemples. Ce livre présente les concepts qui sous-tendent l'apprentissage artificiel, les algorithmes qui en découlent et certaines de leurs applications. Son objectif est de décrire un ensemble d'algorithmes utiles en tentant d'établir un cadre théorique unique pour l'ensemble des techniques regroupées sous ce terme " d'apprentissage artificiel ". A qui s'adresse ce livre ? * Aux décideurs et aux ingénieurs qui souhaitent comprendre l'apprentissage automatique et en acquérir des connaissances solides ; * Aux étudiants de niveau maîtrise, DEA ou école d'ingénieurs qui souhaitent un ouvrage de référence en intelligence artificielle et en reconnaissance des formes.

Download or read book Machine Learning for OpenCV 4 written by Aditya Sharma and published by Packt Publishing Ltd. This book was released on 2019-09-06 with total page 405 pages. Available in PDF, EPUB and Kindle. Book excerpt: A practical guide to understanding the core machine learning and deep learning algorithms, and implementing them to create intelligent image processing systems using OpenCV 4 Key FeaturesGain insights into machine learning algorithms, and implement them using OpenCV 4 and scikit-learnGet up to speed with Intel OpenVINO and its integration with OpenCV 4Implement high-performance machine learning models with helpful tips and best practicesBook Description OpenCV is an opensource library for building computer vision apps. The latest release, OpenCV 4, offers a plethora of features and platform improvements that are covered comprehensively in this up-to-date second edition. You'll start by understanding the new features and setting up OpenCV 4 to build your computer vision applications. You will explore the fundamentals of machine learning and even learn to design different algorithms that can be used for image processing. Gradually, the book will take you through supervised and unsupervised machine learning. You will gain hands-on experience using scikit-learn in Python for a variety of machine learning applications. Later chapters will focus on different machine learning algorithms, such as a decision tree, support vector machines (SVM), and Bayesian learning, and how they can be used for object detection computer vision operations. You will then delve into deep learning and ensemble learning, and discover their real-world applications, such as handwritten digit classification and gesture recognition. Finally, you’ll get to grips with the latest Intel OpenVINO for building an image processing system. By the end of this book, you will have developed the skills you need to use machine learning for building intelligent computer vision applications with OpenCV 4. What you will learnUnderstand the core machine learning concepts for image processingExplore the theory behind machine learning and deep learning algorithm designDiscover effective techniques to train your deep learning modelsEvaluate machine learning models to improve the performance of your modelsIntegrate algorithms such as support vector machines and Bayes classifier in your computer vision applicationsUse OpenVINO with OpenCV 4 to speed up model inferenceWho this book is for This book is for Computer Vision professionals, machine learning developers, or anyone who wants to learn machine learning algorithms and implement them using OpenCV 4. If you want to build real-world Computer Vision and image processing applications powered by machine learning, then this book is for you. Working knowledge of Python programming is required to get the most out of this book.

Download or read book Artificial Intelligence and Machine Learning A Precise Book to Learn Basics written by pc and published by by Mocktime Publication. This book was released on with total page 61 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial Intelligence and Machine Learning - A Precise Book to Learn Basics Table of Contents 1. Introduction to Artificial Intelligence and Machine Learning 1.1 What is Artificial Intelligence? 1.2 The Evolution of Artificial Intelligence 1.3 What is Machine Learning? 1.4 How Machine Learning Differs from Traditional Programming 1.5 The Importance of Artificial Intelligence and Machine Learning 2. Foundations of Machine Learning 2.1 Supervised Learning 2.1.1 Linear Regression 2.1.2 Logistic Regression 2.1.3 Decision Trees 2.2 Unsupervised Learning 2.2.1 Clustering 2.2.2 Dimensionality Reduction 2.3 Reinforcement Learning 2.3.1 Markov Decision Process 2.3.2 Q-Learning 3. Neural Networks and Deep Learning 3.1 Introduction to Neural Networks 3.2 Artificial Neural Networks 3.2.1 The Perceptron 3.2.2 Multi-Layer Perceptron 3.3 Convolutional Neural Networks 3.4 Recurrent Neural Networks 3.5 Generative Adversarial Networks 4. Natural Language Processing 4.1 Introduction to Natural Language Processing 4.2 Preprocessing and Text Representation 4.3 Sentiment Analysis 4.4 Named Entity Recognition 4.5 Text Summarization 5. Computer Vision 5.1 Introduction to Computer Vision 5.2 Image Processing 5.3 Object Detection 5.4 Image Segmentation 5.5 Face Recognition 6. Reinforcement Learning Applications 6.1 Reinforcement Learning in Robotics 6.2 Reinforcement Learning in Games 6.3 Reinforcement Learning in Finance 6.4 Reinforcement Learning in Healthcare 7. Ethics and Social Implications of Artificial Intelligence 7.1 Bias in Artificial Intelligence 7.2 The Future of Work 7.3 Privacy and Security 7.4 The Impact of AI on Society 8. Machine Learning Infrastructure 8.1 Cloud Infrastructure for Machine Learning 8.2 Distributed Machine Learning 8.3 DevOps for Machine Learning 9. Machine Learning Tools 9.1 Introduction to Machine Learning Tools 9.2 Python Libraries for Machine Learning 9.3 TensorFlow 9.4 Keras 9.5 PyTorch 10. Building and Deploying Machine Learning Models 10.1 Building a Machine Learning Model 10.2 Hyperparameter Tuning 10.3 Model Evaluation 10.4 Deployment Considerations 11. Time Series Analysis and Forecasting 11.1 Introduction to Time Series Analysis 11.2 ARIMA 11.3 Exponential Smoothing 11.4 Deep Learning for Time Series 12. Bayesian Machine Learning 12.1 Introduction to Bayesian Machine Learning 12.2 Bayesian Regression 12.3 Bayesian Classification 12.4 Bayesian Model Averaging 13. Anomaly Detection 13.1 Introduction to Anomaly Detection 13.2 Unsupervised Anomaly Detection 13.3 Supervised Anomaly Detection 13.4 Deep Learning for Anomaly Detection 14. Machine Learning in Healthcare 14.1 Introduction to Machine Learning in Healthcare 14.2 Electronic Health Records 14.3 Medical Image Analysis 14.4 Personalized Medicine 15. Recommender Systems 15.1 Introduction to Recommender Systems 15.2 Collaborative Filtering 15.3 Content-Based Filtering 15.4 Hybrid Recommender Systems 16. Transfer Learning 16.1 Introduction to Transfer Learning 16.2 Fine-Tuning 16.3 Domain Adaptation 16.4 Multi-Task Learning 17. Deep Reinforcement Learning 17.1 Introduction to Deep Reinforcement Learning 17.2 Deep Q-Networks 17.3 Actor-Critic Methods 17.4 Deep Reinforcement Learning Applications 18. Adversarial Machine Learning 18.1 Introduction to Adversarial Machine Learning 18.2 Adversarial Attacks 18.3 Adversarial Defenses 18.4 Adversarial Machine Learning Applications 19. Quantum Machine Learning 19.1 Introduction to Quantum Computing 19.2 Quantum Machine Learning 19.3 Quantum Computing Hardware 19.4 Quantum Machine Learning Applications 20. Machine Learning in Cybersecurity 20.1 Introduction to Machine Learning in Cybersecurity 20.2 Intrusion Detection 20.3 Malware Detection 20.4 Network Traffic Analysis 21. Future Directions in Artificial Intelligence and Machine Learning 21.1 Reinforcement Learning in Real-World Applications 21.2 Explainable Artificial Intelligence 21.3 Quantum Machine Learning 21.4 Autonomous Systems 22. Conclusion 22.1 Summary 22.2 Key Takeaways 22.3 Future Directions 22.4 Call to Action

Download or read book Deep Learning written by Stephane S. Tuffery and published by John Wiley & Sons. This book was released on 2023-01-10 with total page 548 pages. Available in PDF, EPUB and Kindle. Book excerpt: A concise and practical exploration of key topics and applications in data science In Deep Learning, from Big Data to Artificial Intelligence, expert researcher Dr. Stéphane Tufféry delivers an insightful discussion of the applications of deep learning and big data that focuses on practical instructions on various software tools and deep learning methods relying on three major libraries: MXNet, PyTorch, and Keras-TensorFlow. In the book, numerous, up-to-date examples are combined with key topics relevant to modern data scientists, including processing optimization, neural network applications, natural language processing, and image recognition. This is a thoroughly revised and updated edition of a book originally released in French, with new examples and methods included throughout. Classroom-tested and intuitively organized, Deep Learning, from Big Data to Artificial Intelligence offers complimentary access to a companion website that provides R and Python source code for the examples offered in the book. Readers will also find: A thorough introduction to practical deep learning techniques with explanations and examples for various programming libraries Comprehensive explorations of a variety of applications for deep learning, including image recognition and natural language processing Discussions of the theory of deep learning, neural networks, and artificial intelligence linked to concrete techniques and strategies commonly used to solve real-world problems Perfect for graduate students studying data science, big data, deep learning, and artificial intelligence, Deep Learning, from Big Data to Artificial Intelligence will also earn a place in the libraries of data science researchers and practicing data scientists.

Download or read book Artificial Intelligence For Dummies written by John Paul Mueller and published by John Wiley & Sons. This book was released on 2018-03-16 with total page 60 pages. Available in PDF, EPUB and Kindle. Book excerpt: Step into the future with AI The term "Artificial Intelligence" has been around since the 1950s, but a lot has changed since then. Today, AI is referenced in the news, books, movies, and TV shows, and the exact definition is often misinterpreted. Artificial Intelligence For Dummies provides a clear introduction to AI and how it’s being used today. Inside, you’ll get a clear overview of the technology, the common misconceptions surrounding it, and a fascinating look at its applications in everything from self-driving cars and drones to its contributions in the medical field. Learn about what AI has contributed to society Explore uses for AI in computer applications Discover the limits of what AI can do Find out about the history of AI The world of AI is fascinating—and this hands-on guide makes it more accessible than ever!

Download or read book TensorFlow pour le Deep learning De la r gr ssion lin aire l apprentissage par renforcement collection O Reilly written by Bharath Ramsundar and published by First Interactive. This book was released on 2018-10-04 with total page 458 pages. Available in PDF, EPUB and Kindle. Book excerpt: Apprenez à résoudre des problèmes d'apprentissage automatique (même difficiles !) avec TensorFIow, la nouvelle bibliothèque logicielle révolutionnaire de Google pour le deep learning. Si vous avez une formation de base en algèbre linéaire et en calcul, ce livre pratique vous introduit dans les arcanes des principes fondamentaux de l'apprentissage automatique en vous montrant comment concevoir des systèmes capables de détecter des objets dans des images, de comprendre du texte et de prédire les propriétés de médicaments potentiels. TensorFlow pour le Deep Learning vous fait découvrir les concepts à l'aide d'exemples pratiques, et vous aide à acquérir des connaissances solides sur le deep learning en partant de cas concrets. Il est idéal pour les développeurs qui ont de l'expérience dans la conception de systèmes logiciels, et sera également utile aux scientifiques et aux autres professionnels qui sont familiers avec la création de scripts, mais pas nécessairement avec la conception d'algorithmes d'apprentissage. • Apprenez les concepts fondamentaux de TensorFlow, y compris comment effectuer un calcul de base • Construisez des systèmes d'apprentissage simples pour comprendre leurs fondements mathématiques • Plongez dans des réseaux profonds entièrement connectés et qui sont utilisés dans des milliers d'applications • Transformez des prototypes en modèles de haute qualité en optimisant des hyperparamètres • Traitez des images avec des réseaux de neurones convolutifs • Gérez des jeux de données en langage naturel avec des réseaux de neurones récurrents • Utilisez l'apprentissage par renforcement pour résoudre des jeux tels que le tic-tac-toe • Entraînez des réseaux profonds avec du matériel performant, qu'il s'agisse de GPU ou d'unités de traitement de tenseurs Collection O'Reilly

Download or read book Intelligence artificielle written by and published by . This book was released on 2019-02-28 with total page 133 pages. Available in PDF, EPUB and Kindle. Book excerpt: Présente sur tous les fronts, dans tous les esprits, l'intelligence artificielle (IA) bouscule ou va bousculer nos conventions, nos philosophies, nos modes de gouvernance. Aucun secteur d'activité ne va y échapper, tant elle transforme les modèles organisationnels de l'entreprise et de la société civile ; modifiant en profondeur les équilibres économiques. Parallèlement, l'hypermédiatisation du sujet (il fait vendre) a quelque peu dévoyé l'expression. A la vue du moindre algorithme, on évoque et invoque l'intelligence artificielle, qu'il s'agisse du fonctionnement des réseaux sociaux, des moteurs de recherche, du suivi des cours de la bourse ou des débats philosophiques... Nous distinguons aujourd'hui deux types d'intelligence artificielle. 1. L'IA faible, la seule qui existe à ce jour, n'a pas conscience d'elle-même et reste très spécialisée par domaine. 2. L'IA forte, générale et non spécialisée, aurait une conscience d'elle-même, voire des émotions ! Un fantasme à ce jour. Si, pour contrebalancer, l'un de ses spécialistes déclare que "l'intelligence artificielle possède moins de sens commun qu'un rat", reconnaissons que l'IA constitue un merveilleux outil offrant en un temps éclair de nombreuses aides à la prise de décision. Pour cela, elle doit apprendre grâce à des algorithmes tels que : le Machine Learning (apprentissage automatique) ; le Deep Learning (apprentissage profond). Concrètement, comment l'intelligence artificielle (qui s'inscrit déjà dans notre quotidien) pourrait-elle changer nos vies ? Elle suscite des interrogations, des craintes, mais aussi des espoirs, notamment chez les personnes handicapées. Cet ouvrage aborde ces questions et y répond en rassemblant les propos des meilleurs spécialistes français.

Download or read book Artificial Intelligence written by Stuart Russell and published by . This book was released on 2020 with total page 1152 pages. Available in PDF, EPUB and Kindle. Book excerpt: The most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. The long-anticipated revision of Artificial Intelligence: A Modern Approach explores the full breadth and depth of the field of artificial intelligence (AI). The 4th Edition brings readers up to date on the latest technologies, presents concepts in a more unified manner, and offers new or expanded coverage of machine learning, deep learning, transfer learning, multiagent systems, robotics, natural language processing, causality, probabilistic programming, privacy, fairness, and safe AI.

Download or read book Machine Learning for Beginners written by Dr. Harsh Bhasin and published by BPB Publications. This book was released on 2023-10-16 with total page 457 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn how to build a complete machine learning pipeline by mastering feature extraction, feature selection, and algorithm training KEY FEATURES ● Develop a solid understanding of foundational principles in machine learning. ● Master regression and classification methods for accurate data prediction and categorization in machine learning. ● Dive into advanced machine learning topics, including unsupervised learning and deep learning. DESCRIPTION The second edition of “Machine Learning for Beginners” addresses key concepts and subjects in machine learning. The book begins with an introduction to the foundational principles of machine learning, followed by a discussion of data preprocessing. It then delves into feature extraction and feature selection, providing comprehensive coverage of various techniques such as the Fourier transform, short-time Fourier transform, and local binary patterns. Moving on, the book discusses principal component analysis and linear discriminant analysis. Next, the book covers the topics of model representation, training, testing, and cross-validation. It emphasizes regression and classification, explaining and implementing methods such as gradient descent. Essential classification techniques, including k-nearest neighbors, logistic regression, and naive Bayes, are also discussed in detail. The book then presents an overview of neural networks, including their biological background, the limitations of the perceptron, and the backpropagation model. It also covers support vector machines and kernel methods. Decision trees and ensemble models are also discussed. The final section of the book provides insight into unsupervised learning and deep learning, offering readers a comprehensive overview of these advanced topics. By the end of the book, you will be well-prepared to explore and apply machine learning in various real-world scenarios. WHAT YOU WILL LEARN ● Acquire skills to effectively prepare data for machine learning tasks. ● Learn how to implement learning algorithms from scratch. ● Harness the power of scikit-learn to efficiently implement common algorithms. ● Get familiar with various Feature Selection and Feature Extraction methods. ● Learn how to implement clustering algorithms. WHO THIS BOOK IS FOR This book is for both undergraduate and postgraduate Computer Science students as well as professionals looking to transition into the captivating realm of Machine Learning, assuming a foundational familiarity with Python. TABLE OF CONTENTS Section I: Fundamentals 1. An Introduction to Machine Learning 2. The Beginning: Data Pre-Processing 3. Feature Selection 4. Feature Extraction 5. Model Development Section II: Supervised Learning 6. Regression 7. K-Nearest Neighbors 8. Classification: Logistic Regression and Naïve Bayes Classifier 9. Neural Network I: The Perceptron 10. Neural Network II: The Multi-Layer Perceptron 11. Support Vector Machines 12. Decision Trees 13. An Introduction to Ensemble Learning Section III: Unsupervised Learning and Deep Learning 14. Clustering 15. Deep Learning Appendix 1: Glossary Appendix 2: Methods/Techniques Appendix 3: Important Metrics and Formulas Appendix 4: Visualization- Matplotlib Answers to Multiple Choice Questions Bibliography

Download or read book Intelligence naturelle et intelligence artificielle written by Association de psychologie scientifique de langue française. Journées d'études and published by Presses Universitaires de France - PUF. This book was released on 1993 with total page 410 pages. Available in PDF, EPUB and Kindle. Book excerpt: Cette édition numérique a été réalisée à partir d'un support physique, parfois ancien, conservé au sein du dépôt légal de la Bibliothèque nationale de France, conformément à la loi n° 2012-287 du 1er mars 2012 relative à l'exploitation des Livres indisponibles du XXe siècle. Pages de début Avant-propos Introduction Première partie - Structure des connaissances en mémoire et représentation des connaissances Les systèmes à bases de connaissances Questions de modélisation et de simulation cognitives L'analyse de la représentation des relations de finalité de causalité et de temps Deuxième partie - Raisonnement naturel et raisonnement sur machine Les mécanismes inférentiels dans le raisonnement humain Une point de vue « artificialiste » sur le raisonnement Troisième partie - Compréhension du langage naturel et traitement sur machine La compréhension du langage par ordinateur La représentation et le traitement cognitif du discours : le rôle des modèles formels L'identification visuelle des mots : expérimentation et modélisation Quatrième partie - Apprentissage naturel, apprentissage automatique symbolique, enseignement intelligemment assisté par ordinateur Approches du morcelage en apprentissage symbolique Le développement de l'enseignement intelligemment assisté par ordinateur Apprentissage : modèles et représentation Modèles de l'apprentissage spatial chez le robot et chez l'animal Cinquième partie - Structures cognitives et néo-connexionnisme Précis de connexionnisme Psychologie de synthèse : les métaphores del'esprit calculateur Pages de fin.

Download or read book Machine Learning written by Ethem Mining and published by . This book was released on 2020-10-25 with total page 496 pages. Available in PDF, EPUB and Kindle. Book excerpt: If you are looking for a comprehensive guide that explains in a simple way how to manage machine learning and AI, please keep reading. What do you need to learn to move from being a complete beginner to someone with advanced knowledge of machine learning? Have you ever wondered how to leverage big data from big tech companies (Google, Facebook e Amazon) to reach your objectives? Do you want to understand which ones are the best libraries to use and why is Python considered the best language for machine learning? The term Machine Learning refers to the capability of a machine to learn something without any pre existing program. Automatic learning is a way to educate an algorithm to learn from various environmental situations. Machine learning involves the usage of enormous quantities of data and an efficient algorithm enabled to adapt and enhance its capabilities according to recurring situations. From banking operations to online shopping and also on social media, we daily use machine learning data algorithms to make our experience more efficient, simple and secure. Machine learning and its capabilities are rapidly becoming popular - we have just discovered part of its potential. This bundle will give you all the information you need in order to leverage your knowledge and give you an excellent level of education. All the subjects will be supported by examples and practical exercises that will enable you to reinforce your level of knowledge Specifically you will learn What does Machine Learning and Artificial Intelligence mean Machine Learning evolution Machine learning applications Difference between AI and Machine Learning Big Data Connection between Machine Learning and Big Data How to use Big Data from large size companies to make your business scalable How to acquire new customers via simple marketing strategies Python Programming Advanced programming techniques and much more. This manual has been written to meet all levels of education. If your level of knowledge is low and you don't have any previous experience, this book will empower you to learn key functionalities and navigate through various subjects smoothly. If you have already a good understanding, you will find useful insights that will help to enhance your competences. If you want to learn Machine Learning but don't know where to start... Buy Now to get started!

Download or read book Machine Learning written by Samuel Hack and published by . This book was released on 2019-11-21 with total page 636 pages. Available in PDF, EPUB and Kindle. Book excerpt: Master the world of Python and Machine Learning with this incredible 4-in-1 bundle. Are you interested in becoming a Python pro? Or do you want to learn more about the incredible world of machine learning, and what it can do for you? Then keep reading. Created with the beginner in mind, this powerful bundle delves into the fundamentals behind Python and Machine Learning, from basic code and mathematical formulas to complex neural networks and ensemble modeling. Inside, you'll discover everything you need to know to get started with Python and Machine Learning, and begin your journey to success! In book one, MACHINE LEARNING FOR BEGINNERS, you'll learn: What is Artificial Intelligence Really, and Why is it So Powerful? Choosing the Right Kind of Machine Learning Model for You An Introduction to Statistics Reinforcement Learning and Ensemble Modeling "Random Forests" and Decision Trees And Much More! In book two, MACHINE LEARNING MATHEMATICS, you will: Learn the Fundamental Concepts of Machine Learning Algorithms Understand The Four Fundamental Types of Machine Learning Algorithm Master the Concept of "Statistical Learning" Learn Everything You Need to Know about Neural Networks and Data Pipelines Master the Concept of "General Setting of Learning" And Much More! In book three, LEARNING PYTHON, you'll discover: How to Install, Run, and Understand Python on Any Operating System A Comprehensive Introduction to Python Python Basics and Writing Code Writing Loops, Conditional Statements, Exceptions and More Python Expressions and The Beauty of Inheritances And More! And in book four, PYTHON MACHINE LEARNING, you will: Learn the Fundamentals of Machine Learning Master the Nuances of 12 of the Most Popular and Widely-Used Machine Learning Algorithms Become Familiar with Data Science Technology Dive Into the Functioning of Scikit-Learn Library and Develop Machine Learning Models Uncover the Secrets of the Most Critical Aspect of Developing a Machine Learning Model - Data Pre-Processing and Training/Testing Subsets And So Much More! Whether you're a complete beginner or a programmer looking to improve your skillset, this bundle is your all-in-one solution to mastering the world of Python and Machine Learning. So don't wait - it's never been easier to learn. Buy now to become a master of Python and machine learning today! Scroll Up and Click the BUY NOW Button to Get Your Copy!