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PDZ domains are found in one to seven copies in scaffolding proteins that cluster together ion channels and signal transduction proteins at synapses, in photoreceptors, and in polarized epithelial...

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Deep neural networks (DNNs) represent promising approaches to molecular machine learning (ML). However, their applicability remains limited to single-component materials and a general DNN model capable of handling various multicomponent molecular systems with composition data is still elusive, while current ML approaches for multicomponent molecular systems are still molecular descriptor-based ...

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Ordinal parameters have a domain of a finite set of values which are either in- teger and/or real values. For example, the sets {1, 5, 8} and {3. 4, 2. 5, 6, 9. 1} are possible domains of ordinal parameters. Ordinal values must have an ordering by the less-than opera- tor.

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Here we illustrate how tuning along Bayesian reasoning, or Bayesian Optimisation, can bring down the time spent to get to the optimal set of parameters.

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myBopt = GPyOpt.methods.BayesianOptimization(f=f, domain=bounds) myBopt.run_optimization(max_iter=100) print(myBopt.x_opt) print(myBopt.fx_opt). I want to add...
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A Gentle Introduction to the GPyOpt Module Manually tuning hyperparameters in a neural network is slow and boring. Using Bayesian Optimisation to do it for you is slightly less slower and you can go do other things whilst it’s running.
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