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The tasks of location, classification, and segmentation are known and applied by astronomers in various problems such as: Morphological classification of galaxies, Transient detection, search for supernovae among others.

Is widely known in this decade will see a series of astronomical mega-projects coming into operation producing complex data whose dimensionality and volume will exceed any current scale. This requires the application of a new generation of machine learning (Deeplearning) models for classification, location, and segmentation. In this tutorial we will cover the latest advances in Deep learning applied to Semantic segmentation, Object localization and Instance Segmentation. The tutorial modality will be divided into blocks of 30 minutes as follows:

• Part 1: Introduction (Theory)
• Part 2: Semantic segmentation (U-NET)
• Part 3: Object localization (YOLO3)
• Part 4: Instance Segmentation (Mask R-CNN)
• Prerequisites: Intermediate Python knowledge is strongly recommended.

Level: We assume you are comfortable with deep learning basics as layers, neuron, activation function, loss function among other basics concepts.

Note: The notebooks are made to be run on google collab