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SUMMARY:Introduction to Deep Learning
DESCRIPTION:Register latest by: 12 Feb 2026\nAbout the event\nThis event is organised by: Mimer AI Factory & LUMI AI Factory (CSC) \nDeep learning is a powerful subset of machine learning where computers learn patterns from data\, similar to how our brains learn. It uses artificial neural networks – systems inspired by biological neurons that process information through many layers. The term “deep” refers to networks with tens or hundreds of layers\, each containing millions of connections. Deep learning today powers technologies ranging from foundational applications such as language models and image recognition\, to cutting edge applications such as weather forecasting and protein folding. However\, for beginners\, stepping into this field can feel daunting and we intend to make this easy for you. \nThis online workshop\, organized by Mimer\, in partnership with LUMI AI Factory\, is designed to provide a beginner-friendly introduction to deep learning concepts\, workflows\, architectures\, and practical applications. You will learn end-to-end approaches for: \n\n\ntackling AI tasks including classification and regression \n\n\nbuilding deep model architectures such as Convolutional Neural Networks (CNNs) \n\n\napply advanced training techniques such as transfer learning \n\n\nWho is this for? \n\nStudents and early-career researchers in computer science\, bioinformatics\, natural sciences\, engineering\, or related fields\nData scientists working on deep learning-based applications\nAspiring software developers looking to learn foundational skills in AI\n\nKey takeaways for participants: \nA gentle introduction to deep learning fundamentals\, covering: \n\nCore concepts and terminology\n\nSteps in a deep learning workflow using Python and Keras (with Tensorflow\, and potentially also with PyTorch as backend) \n\nData preparation for training\nImplementing a basic neural network\nMonitoring and troubleshooting the training process\nVisualizing results and evaluating model performance\n\nPrerequisites: \n\n\n\nBasic Python programming skills and familiarity with packages like NumPy\, Pandas\, and Matplotlib\nExperience working with Jupyter notebooks (recommended but not mandatory)\n\n\n\nSchedule\nAll times in CET (Europe/Stockholm time) \n\n\n\nDay\nTime\nContents\n\n\n\n\n2026-02-16\n9:00 – 11:00\nSetup and dry-run\n\n\n2026-02-17\n9:00 – 12:00\nIntroduction;\nClassification by a neural network using Keras\n\n\n2026-02-18\n9:00 – 12:00\nMonitoring the training process\n\n\n2026-02-19\n9:00 – 12:00\nAdvanced Layer types; Transfer learning\n\n\n\nInstructors and Teaching Assistants\n\nAshwin Mohanan\, Mimer AIF / RISE\nFrancesco Fiusco\, Mimer AIF / RISE\nKatja Mankinen\, LUMI AIF / CSC\nLodovico Giaretta\, Mimer AIF / RISE\nMarlon Tobaben\, LUMI AIF / CSC\nOskar Taubert\, LUMI AIF / CSC\nYonglei Wang\, Mimer AIF / NAISS
URL:https://mimer-ai.eu/event/introduction-to-deep-learning/
ATTACH;FMTTYPE=image/jpeg:https://mimer-ai.eu/wp-content/uploads/2026/01/Introduction-to-deep-learning.jpg
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