{"id":null,"code":"TKO_7094","name":{"valueFi":"Introduction to Deep Learning","valueEn":"Introduction to Deep Learning","valueSv":"Introduction to Deep Learning"},"credits":5.0,"minCredits":5,"maxCredits":5,"tags":[],"createdAt":1790534321796,"contentList":[{"title":{"valueFi":"Osaamistavoitteet","valueEn":"Learning outcomes","valueSv":""},"content":{"valueFi":"Solid understanding of what deep learning is, when it’s applicable, and what its limitations are. Standard workflow for approaching and solving machine-learning problems and knowledge how to address commonly encountered issues. Ability to use Keras to tackle real-world problems ranging from computer vision to natural-language processing.","valueEn":"Solid understanding of what deep learning is, when it’s applicable, and what its limitations are. Standard workflow for approaching and solving machine-learning problems and knowledge how to address commonly encountered issues. Ability to use Keras to tackle real-world problems ranging from computer vision to natural-language processing.","valueSv":"Solid understanding of what deep learning is, when it’s applicable, and what its limitations are. Standard workflow for approaching and solving machine-learning problems and knowledge how to address commonly encountered issues. Ability to use Keras to tackle real-world problems ranging from computer vision to natural-language processing."}},{"title":{"valueFi":"Sisältö","valueEn":"Content","valueSv":""},"content":{"valueFi":"- Introduction: motivation, history of deep learning, deep learning today.\r\n- Basics of model training.\r\n- Gradient descent and backpropagation.\r\n- Theoretical connection between maximum likelihood estimation and loss functions.\r\n- Learning tasks (classification and regression) and output activations.\r\n- Stochastic gradient descent, momentum and learning rate scheduling.\r\n- Adaptive optimizers.\r\n- Initialization and normalization.\r\n- Regularization techniques.\r\n- Basics of convolutional neural networks (CNNs).\r\n- Advanced CNN architectures.\r\n- Transfer learning.\r\n- Sequential data and natural language processing.","valueEn":"- Introduction: motivation, history of deep learning, deep learning today.\r\n- Basics of model training.\r\n- Gradient descent and backpropagation.\r\n- Theoretical connection between maximum likelihood estimation and loss functions.\r\n- Learning tasks (classification and regression) and output activations.\r\n- Stochastic gradient descent, momentum and learning rate scheduling.\r\n- Adaptive optimizers.\r\n- Initialization and normalization.\r\n- Regularization techniques.\r\n- Basics of convolutional neural networks (CNNs).\r\n- Advanced CNN architectures.\r\n- Transfer learning.\r\n- Sequential data and natural language processing.","valueSv":"- Introduction: motivation, history of deep learning, deep learning today.\r\n- Basics of model training.\r\n- Gradient descent and backpropagation.\r\n- Theoretical connection between maximum likelihood estimation and loss functions.\r\n- Learning tasks (classification and regression) and output activations.\r\n- Stochastic gradient descent, momentum and learning rate scheduling.\r\n- Adaptive optimizers.\r\n- Initialization and normalization.\r\n- Regularization techniques.\r\n- Basics of convolutional neural networks (CNNs).\r\n- Advanced CNN architectures.\r\n- Transfer learning.\r\n- Sequential data and natural language processing."}},{"title":{"valueFi":"Suoritustavat","valueEn":"Study methods","valueSv":""},"content":{"valueFi":"Written exam or project work.","valueEn":"Written exam or project work","valueSv":""}},{"title":{"valueFi":"Toteutustavat","valueEn":"Course unit methods","valueSv":""},"content":{"valueFi":"Video lectures, voluntary homework, discussion on course piazza and weekly meetings (remotely or on campus).","valueEn":"Video lectures, voluntary homework, discussion on course piazza and weekly meetings (remotely or on campus).","valueSv":""}},{"title":{"valueFi":"Oppimateriaalit","valueEn":"Learning material","valueSv":""},"content":{"valueFi":"Deep Learning Book (https://www.deeplearningbook.org/)\r\n\r\n\r\nHands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 3rd Edition (https://www.oreilly.com/library/view/hands-on-machine-learning/9781098125967/)\r\n\r\n\r\nVideo lectures and lecture notes, see https://users.utu.fi/knuutila/ for more information.","valueEn":"Deep Learning Book (https://www.deeplearningbook.org/)\r\n\r\n\r\nHands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 3rd Edition (https://www.oreilly.com/library/view/hands-on-machine-learning/9781098125967/)\r\n\r\n\r\nVideo lectures and lecture notes, see https://users.utu.fi/knuutila/ for more information.","valueSv":""}},{"title":{"valueFi":"Lisätiedot","valueEn":"Further information","valueSv":""},"content":{"valueFi":"Good knowledge of Python programming and numpy is beneficial.","valueEn":"Good knowledge of Python programming and numpy is beneficial.","valueSv":""}},{"title":{"valueFi":"Kurssikirjallisuus","valueEn":"Literature","valueSv":""},"content":{"valueFi":"","valueEn":"","valueSv":""}},{"title":{"valueFi":"Esitietovaatimukset","valueEn":"Qualifications","valueSv":""},"content":{"valueFi":"TKO_2115 Tekoälyn menetelmät\r\nTKO_3107 Tietorakenteet ja algoritmit","valueEn":"TKO_2115 Artificial Intelligence: Methods\r\nTKO_3107 Data structures and Algorithms","valueSv":""}},{"title":{"valueFi":"Arviointiasteikko","valueEn":"Assessment scale","valueSv":""},"content":{"valueFi":"0-5","valueEn":"0-5","valueSv":"0-5"}},{"title":{"valueFi":"Arviointikriteerit","valueEn":"Assessment criteria","valueSv":""},"content":{"valueFi":"","valueEn":"","valueSv":""}},{"title":{"valueFi":"Arviointikriteerit 2","valueEn":"Assessment criteria 2","valueSv":""},"content":{"valueFi":"","valueEn":"","valueSv":""}},{"title":{"valueFi":"Arviointikriteerit 3","valueEn":"Assessment criteria 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