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L'invention de ChatGPT est comparable à l'invention de l'imprimerie ou d'Internet. Elle serait même, selon l'auteur, aussi révolutionnaire que le fut l'invention de l'écriture... Dans un contexte effervescent, où les critiques fusent souvent sans discernement ni rationalité, cet ouvrage apporte des éléments de réponses indispensables. Comment fonctionnent les nouvelles formes d'intelligence artificielle ? Que faire concrètement avec ces outils ? Quelles perspectives ouvrent-ils ? Mais aussi : quels impacts ont-ils sur la vie des citoyens, sur leur environnement professionnel et, plus largement, sur la démocratie et les équilibres économiques ou géopolitiques ? Enfin, comment l'Europe peut-elle répondre à l'enjeu central de son autonomie numérique ? Un essai capital pour sortir de la fascination, éveiller les consciences et (enfin) comprendre le dessous des cartes.

Fuzzy logic --- Big data --- Deep learning (Machine learning) --- Chatbots --- Artificial intelligence --- Natural language processing (Computer science)

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An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

Probability theory --- Information systems --- Artificial intelligence. Robotics. Simulation. Graphics --- Mathematical linguistics --- analyse (wiskunde) --- Machine learning. --- Artificiële intelligentie --- Machine learning --- Learning, Machine --- Artificial intelligence --- Machine theory --- למידה חשובית --- Apprentissage automatique --- Machine Learning --- Apprentissage automatique. --- Transfer Learning --- Learning, Transfer --- Machinaal leren --- 681.3*I2 --- 681.3*I2 Artificial intelligence. AI --- Artificial intelligence. AI --- deep learning --- machine learning --- artificiële intelligentie (AI) --- Informatique --- Intelligence artificielle

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This book covers both classical and modern models in deep learning. The chapters of this book span three categories: The basics of neural networks: Many traditional machine learning models can be understood as special cases of neural networks. An emphasis is placed in the first two chapters on understanding the relationship between traditional machine learning and neural networks. Support vector machines, linear/logistic regression, singular value decomposition, matrix factorization, and recommender systems are shown to be special cases of neural networks. These methods are studied together with recent feature engineering methods like word2vec. Fundamentals of neural networks: A detailed discussion of training and regularization is provided in Chapters 3 and 4. Chapters 5 and 6 present radial-basis function (RBF) networks and restricted Boltzmann machines. Advanced topics in neural networks: Chapters 7 and 8 discuss recurrent neural networks and convolutional neural networks. Several advanced topics like deep reinforcement learning, neural Turing machines, Kohonen self-organizing maps, and generative adversarial networks are introduced in Chapters 9 and 10. The book is written for graduate students, researchers, and practitioners. Numerous exercises are available along with a solution manual to aid in classroom teaching. Where possible, an application-centric view is highlighted in order to provide an understanding of the practical uses of each class of techniques.

Information systems --- Artificial intelligence. Robotics. Simulation. Graphics --- neuronale netwerken --- machine learning --- deep learning --- Artificial intelligence. --- Computers. --- Microprocessors. --- Machine learning. --- Neural networks (Computer science) --- Artificial Intelligence. --- Information Systems and Communication Service. --- Processor Architectures. --- Artificial neural networks --- Nets, Neural (Computer science) --- Networks, Neural (Computer science) --- Neural nets (Computer science) --- Artificial intelligence --- Natural computation --- Soft computing --- Learning, Machine --- Machine theory --- Minicomputers --- Automatic computers --- Automatic data processors --- Computer hardware --- Computing machines (Computers) --- Electronic brains --- Electronic calculating-machines --- Electronic computers --- Hardware, Computer --- Computer systems --- Cybernetics --- Calculators --- Cyberspace --- AI (Artificial intelligence) --- Artificial thinking --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers