#计算机科学#Automated Deep Learning: Neural Architecture Search Is Not the End (a curated list of AutoDL resources and an in-depth analysis)
#计算机科学#Train, Evaluate, Optimize, Deploy Computer Vision Models via OpenVINO™
#计算机科学#An autoML framework & toolkit for machine learning on graphs.
#计算机科学#DEEPScreen: Virtual Screening with Deep Convolutional Neural Networks Using Compound Images
#计算机科学#A paper collection about automated graph learning
Students Performance Evaluation using Feature Engineering, Feature Extraction, Manipulation of Data, Data Analysis, Data Visualization and at lat applying Classification Algorithms from Machine Learni...
Nature-inspired algorithms for hyper-parameter tuning of Scikit-Learn models.
#计算机科学#Convenient classes for optimizing Hyper-parameters, using Random search, Spearmint and SigOpt
Combined hyper-parameter optimization and feature selection for machine learning models using micro genetic algorithms
A gradient free optimization routine which combines Particle Swarm Optimization with a local optimization for each particle
#计算机科学#Grammaropt : a framework for optimizing over domain specific languages (DSLs)
#计算机科学#Pipelineopt, sckit-learn automatic pipeline optimization
Pipoh is a library that implements several diversification techniques base on mean-variance framework. In addition, it includes a novel purely data-driven methods for determining the optimal value of ...
Python implementation that explores how different parameters impact a single hidden layer of a feed-forward neural network using gradient descent
#计算机科学#To utilize the Breast Cancer Wisconsin Dataset for machine learning purposes. The aim is to diagnose breast cancer by employing a supervised binary, distance-based classifier (K Nearest Neighbours), w...
#计算机科学#Hyper-Parameter Optimisation experiment as part of my undergraduate dissertation (2019)
Students Performance Evaluation using Feature Engineering, Feature Extraction, Manipulation of Data, Data Analysis, Data Visualization and at lat applying Classification Algorithms from Machine Learni...