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mehlig b machine learning neural networks intr 2021

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Textbook in PDF format ACKNOWLEDGEMENTS This textbook is based on lecture notes for the course Artificial Neural Networks that I have given at Gothenburg University and at Chalmers Technical University in Gothenburg, Sweden. When I prepared my lectures, my main source was Introduction to the theory of neural computation by Hertz, Krogh, and Palmer [1]. Other sources were Neural Networks: a comprehensive foundation by Haykin [2], Horner’s lecture notes [3] from Heidelberg, Deep learning by Goodfellow, Bengio & Courville [4], and also the online book Neural Networks and Deep Learning by Nielsen [5]. I thank Martin Cejka for typesetting the first version of my hand-written lecture ˇ notes, Erik Werner and Hampus Linander for their help in preparing Chapter 8, Kristian Gustafsson for his detailed feedback on Chapter 11, Nihat Ay for his comments on Section 4.5, and Mats Granath for discussions about autoencoders. I would also like to thank Juan Diego Arango, Oleksandr Balabanov, Anshuman Dubey, Johan Fries, Phillip Gräfensteiner, Navid Mousavi, Marina Rafajlovic, Jan Schiffeler, Ludvig Storm, and Arvid Wenzel Wartenberg for implementing the algorithms described in this book. Many Figures are based on their results. Oleksandr Balabanov, Anshuman Dubey, Jan Meibohm, and in particular Johan Fries and Marina Rafajlovic contributed exam questions that became exercises in this book. Finally, I would like to express my gratitude to Stellan Östlund, for his encouragement and criticism. Last but not least, a large number of colleagues and students – past and present – pointed out misprints and errors, and suggested improvements. I thank them all

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mehlig b machine learning neural networks intr 2021