Deep learning¶ Our toy example will be to classify species as endangered or not based genomic data. The rationale is that species with a small (effective population size) will have higher chances to be threatened. The amount of genomic variability (e.g. polymorphic sites and haplotype diversity) is taken as a proxy for the (effective) population […]
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Deep learning¶ Our toy example will be to classify species as endangered or not based genomic data. The rationale is that species with a small (effective population size) will have higher chances to be threatened. The amount of genomic variability (e.g. polymorphic sites and haplotype diversity) is taken as a proxy for the (effective) population
程序代写代做 Keras graph compiler deep learning kernel Deep learning¶ Read More »
Assignment: Reinforcement Learning and Deep Learning Contents Part 1: Q-learning (Snake) Provided Snake Environment Q-learning Agent Debug Convenience Part 2: Deep Learning (MNIST Fashion) Background Neural Network Implementation Details Testing What to Report Warning/Hints Deliverables Report Checklist Part 1: Q-learning (Snake) Snake is a famous video game originated in the 1976 arcade game Blockade. The
Food Classification Challenge All assignment work must use the Python/Keras CIPA1 development environment. Deliverables The main purpose of this assignment is to introduce the student to the practical aspects of developing advanced deep learning based computer vision systems within the Python/Keras environment. The assignment is worth 40% of your overall module mark2.
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