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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

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AI-technologieën bieden verbluffende mogelijkheden op allerlei vlakken in onze levens. Oók op het gebied van leerprocessen. Maar er kleven ook ethische risico’s aan het gebruik van AI-systemen. Een van de hardnekkige misverstanden over AI is dat deze technologie zichtzelf als entiteit ontwikkelt. Echter: AI wordt door mensen ontwikkeld; door mensen met normen en waarden, die ethische overwegingen en beslissingen nemen die de huidige en toekomstige systemen vormgeven. Met het nieuwe handboek Leven en leren met AI, geschreven door ICT-docenten, ontdekken studenten hoe zij generatieve AI in hun dagelijks leven en in hun leerproces verantwoord kunnen inzetten. Daarvoor is het nodig dat zij eerst de basisprincipes en concepten achter AI leren doorgronden. (source: publisher's website)

Artificial intelligence. Robotics. Simulation. Graphics --- neuronale netwerken --- mens-machine communicatie --- deep learning --- AI (artificiële intelligentie) --- Mass communications

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Deep learning (Machine learning) --- Learning, Deep (Machine learning) --- Iterative methods (Mathematics) --- Machine learning --- Telecommunication technology --- Computer architecture. Operating systems --- Information systems --- Artificial intelligence. Robotics. Simulation. Graphics

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Discover the concepts of deep learning used for natural language processing (NLP), with full-fledged examples of neural network models such as recurrent neural networks, long short-term memory networks, and sequence-2-sequence models. You’ll start by covering the mathematical prerequisites and the fundamentals of deep learning and NLP with practical examples. The first three chapters of the book cover the basics of NLP, starting with word-vector representation before moving onto advanced algorithms. The final chapters focus entirely on implementation, and deal with sophisticated architectures such as RNN, LSTM, and Seq2seq, using Python tools: TensorFlow, and Keras. Deep Learning for Natural Language Processing follows a progressive approach and combines all the knowledge you have gained to build a question-answer chatbot system. This book is a good starting point for people who want to get started in deep learning for NLP. All the code presented in the book will be available in the form of IPython notebooks and scripts, which allow you to try out the examples and extend them in interesting ways. You will: Gain the fundamentals of deep learning and its mathematical prerequisites Discover deep learning frameworks in Python Develop a chatbot Implement a research paper on sentiment classification.

Programming --- Artificial intelligence. Robotics. Simulation. Graphics --- Mathematical linguistics --- neuronale netwerken --- NLP (neurolinguïstisch programmeren) --- Python (informatica) --- bots --- deep learning --- KI (kunstmatige intelligentie) --- AI (artificiële intelligentie)

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"Explore the ever-expanding, fascinating field of Artificial Intelligence and its latest technologies with this industry-leading text. Artificial Intelligence: A Modern Approach, Global Edition, 4th Edition by Stuart Russel and Peter Norvigis the long-anticipated revision of this market-leading text, exploring the full breadth and depth of the field of Artificial Intelligence (AI). From robotic planetary explorers to online services with billions of users, the textbook covers a wide range of applications, delving into the advanced methods of reasoning, deep learning, perception and mathematics. Thoroughly updated and with new content, this latest edition brings you up to date on the latest technological advancements in the field, presenting concepts in a more unified manner. Some of the changes in the content include: Content that focuses deeper on machine learning rather than the hand-crafted knowledge of engineering. An updated, thorough discussion emphasises deep learning, probabilistic programming, and multi-agent systems. Extensive updates on the Robotics chapter now include content regarding the interaction of robots with humans. A new online site now includes all the exercises for this edition, allowing the team of authors to update and improve them continuously. Besides studying the methods and technologies, this edition also considers the ethical aspects and values of practicing the discipline. Fairness, integrity, respect, and social good, provide a fundamental framework to the learning process in this edition, studying the impact of AI on society. With a plethora of topics, exercises, and practical applications, this leading text is the must-read edition of this field, offering a deeper understanding and a multi-faceted approach to this expanding subject."

Artificial intelligence --- Artificial intelligence. --- Artificial intelligence. Robotics. Simulation. Graphics --- artificial intelligence --- deep learning --- machine learning --- artificiële intelligentie (AI) --- robotica --- computer vision --- (zie ook: automatisering) --- KI (kunstmatige intelligentie) --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Machine theory --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- 681.3*I2 --- 681.3*I2 Artificial intelligence. AI --- Artificial intelligence. AI --- AI (artificiële intelligentie)