AI/ML Applications on Gadi - Astronomy
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Course Information
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Content Information
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Astronomy Background and Usage of Summary Statistics (16:16)
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Basics of Neutral Networks - Neurons, Activation Functions and Types of Networks (21:35)
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Continue Basics of Neural Networks - Loss Functions, Learning Rate and the Optimiser; Introduce the Bayesian Inference and Samplers (22:45)
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Launching ARE and Inspecting the HERA Dataset (24:23)
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Continue Inspecting the HERA Dataset; Introduce the Published Inference Using 21cm FAST (21:23)
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Clean and Build Training Datasets (23:05)
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Further Data Preparation (13:24)
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Train the Network (23:08)
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Continue Training the Network and Inspecting the Training Progress (20:37)
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Make Predictions Using the Training Emulator (15:37)
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Write Scripts to Do Bayesian Inference Using EMCEE (20:50)
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Continue Setup of EMCEE and Running MCMC (14:10)
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Inspect the MCMC Results and a Brief Introduction to the MultiNest Sampler (22:41)
