## Hyperparameter Search With GPyOpt: Part 3 – Keras (CNN) Classification and Ensembling

GPyOpt is a Python open-source library for Bayesian Optimization developed by the Machine Learning group of the University of Sheffield. It is based on GPy, a Python framework for Gaussian process modelling.

In this article, we demonstrate how to use this package to perform hyperparameter search for a classification problem with Keras.

## Ebook: Deep Learning for Toxicity and Disease Prediction – Gong, Zhang, Chen (eds) 2020

Deep Learning for Toxicity and Disease Prediction is a collection of 12 articles published as a free ebook.

## Hyperparameter Search With GPyOpt: Part 2 – XGBoost Classification and Ensembling

GPyOpt is a Python open-source library for Bayesian Optimization developed by the Machine Learning group of the University of Sheffield. It is based on GPy, a Python framework for Gaussian process modelling.

In this article, we demonstrate how to use this package to perform hyperparameter search for a classification problem with XGBoost.

## Paper: Seq2seq Fingerprint: An Unsupervised Deep Molecular Embedding for Drug Discovery – Xu et al 2017

In this paper, we propose a novel unsupervised molecular embedding method, providing a continuous feature vector for each molecule to perform further tasks, e.g., solubility classification. In the proposed method, a multi-layered Gated Recurrent Unit (GRU) network is used to map the input molecule into a continuous feature vector of fixed dimensionality, and then another deep GRU network is employed to decode the continuous vector back to the original molecule.

## Paper: CheMixNet: Mixed DNN Architectures for Predicting Chemical Properties using Multiple Molecular Representations – Paul et al 2018

In this interesting paper, the authors use a multi input neural network to predict various small molecule properties in which one branch is a multilayer perceptron with MACCS fingerprints and the other is one of a RNN, 1D CNN, 1D CNN-RNN with SMILES.

## Paper: A Tutorial on Bayesian Optimization – Frazier 2018

In this tutorial, we describe how Bayesian optimization works, including Gaussian process regression and three common acquisition functions: expected improvement, entropy search, and knowledge gradient. We conclude with a discussion of Bayesian optimization software and future research directions in the field.

## Hyperparameter Search With GPyOpt: Part 1 – Scikit-learn Classification and Ensembling

GPyOpt is a Python open-source library for Bayesian Optimization developed by the Machine Learning group of the University of Sheffield. It is based on GPy, a Python framework for Gaussian process modelling.

In this article, we demonstrate how to use this package to do hyperparameter search for a classification problem with Scikit-learn.

## Paper: Keep It Simple: Graph Autoencoders Without Graph Convolutional Networks – Salha et al 2019

In this work, the authors present a simpler alternative, “a simple linear model w.r.t. the adjacency matrix of the graph”, to graph convolution encoders for graph autoencoders.

## Hyperparameter Search (And Pruning) With Optuna: Part 5 – Keras (CNN) Classification and Ensembling

In addition to using the tree-structured Parzen algorithm via Optuna to find hyperparameters for a CNN with Keras for the the MNIST handwritten digits data set classification problem, we add asynchronous successive halving, a pruning algorithm, to halt training when preliminary results are unpromising.

## Paper: A self-attention based message passing neural network for predicting molecular lipophilicity and aqueous solubility – Tang et al 2020

In this paper, we describe a self-attention-based message-passing neural network (SAMPN) model, which is a modification of Deepchem’s MPN [16] and is state-of-the-art in deep learning. It directly learns the most relevant features of each QSAR/QSAPR task in the learning process and assigns the degree of importance for substructures to improve the interpretability of prediction.

## Hyperparameter Search (And Pruning) With Optuna: Part 4 – XGBoost Classification and Ensembling

In addition to using the tree-structured Parzen algorithm via Optuna to find hyperparameters for XGBoost for the the MNIST handwritten digits data set classification problem, we add asynchronous successive halving, a pruning algorithm, to halt training when preliminary results are unpromising.

## Paper: A Deep Learning Approach to Antibiotic Discovery – Stokes et al 2020

This is a very interesting paper in which the authors use a message passing neural network on a carefully selected data set to predict antibacterial activity against E. coli. Then they apply their model to other data sets, while also prioritizing molecules that are different (via minimum Tanimoto similarity) from existing antibiotics, to find candidates for new antibiotics.

## Hyperparameter Search With Optuna: Part 3 – Keras (CNN) Classification and Ensembling

In this article, we use the tree-structured Parzen algorithm via Optuna to find hyperparameters for a convolutional neural network (CNN) with Keras for the the MNIST handwritten digits data set classification problem.

## Hyperparameter Search With Optuna: Part 2 – XGBoost Classification and Ensembling

In this article, we use the tree-structured Parzen algorithm via Optuna to find hyperparameters for XGBoost for the the MNIST handwritten digits data set classification problem.

## Hyperparameter Search With Optuna: Part 1 – Scikit-learn Classification and Ensembling

Optuna is a Python package for general function optimization. It also has specialized coding to integrate it with many popular machine learning packages to allow the use of pruning algorithms to make hyperparameter searching more efficient. In this article we use Optuna to optimize hyperparameters for Sci-kit Learn machine learning algorithms.

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