[图书][B] Hyperparameter tuning for machine and deep learning with R: A practical guide
This open access book provides a wealth of hands-on examples that illustrate how
hyperparameter tuning can be applied in practice and gives deep insights into the working …
hyperparameter tuning can be applied in practice and gives deep insights into the working …
Machine learning-based new approach to films review
The main purpose of Sentiment Analysis (SA) is to derive useful insights from large amounts
of unstructured data compiled from various sources. This analysis helps to interpret and …
of unstructured data compiled from various sources. This analysis helps to interpret and …
An end-to-end learning approach for enhancing intrusion detection in Industrial-Internet of Things
Abstract The Industrial-Internet of Things (I-IoT) stands out as one of the most dynamically
evolving subfields within the expansive realm of the Internet of Things (IoT). Its exponential …
evolving subfields within the expansive realm of the Internet of Things (IoT). Its exponential …
Nucleic acid quantification by multi-frequency impedance cytometry and machine learning
Determining nucleic acid concentrations in a sample is an important step prior to proceeding
with downstream analysis in molecular diagnostics. Given the need for testing DNA amounts …
with downstream analysis in molecular diagnostics. Given the need for testing DNA amounts …
Deepcave: An interactive analysis tool for automated machine learning
Automated Machine Learning (AutoML) is used more than ever before to support users in
determining efficient hyperparameters, neural architectures, or even full machine learning …
determining efficient hyperparameters, neural architectures, or even full machine learning …
Flow field reconstruction from sparse sensor measurements with physics-informed neural networks
MY Hosseini, Y Shiri - Physics of Fluids, 2024 - pubs.aip.org
In the realm of experimental fluid mechanics, accurately reconstructing high-resolution flow
fields is notably challenging due to often sparse and incomplete data across time and space …
fields is notably challenging due to often sparse and incomplete data across time and space …
Large Language Model Agent for Hyper-Parameter Optimization
Hyperparameter optimization is critical in modern machine learning, requiring expert
knowledge, numerous trials, and high computational and human resources. Despite the …
knowledge, numerous trials, and high computational and human resources. Despite the …
Improving accuracy of interpretability measures in hyperparameter optimization via Bayesian algorithm execution
Despite all the benefits of automated hyperparameter optimization (HPO), most modern HPO
algorithms are black-boxes themselves. This makes it difficult to understand the decision …
algorithms are black-boxes themselves. This makes it difficult to understand the decision …
Hyperparameter selection for physics-informed neural networks (PINNs)–Application to discontinuous heat conduction problems
P Sharma, L Evans, M Tindall… - Numerical Heat Transfer …, 2023 - Taylor & Francis
In recent years, physics-informed neural networks (PINNs) have emerged as an alternative
to conventional numerical techniques to solve forward and inverse problems involving …
to conventional numerical techniques to solve forward and inverse problems involving …
A Novel Approach for Deep Learning-Powered Forecasting of Market Bottoms in Cryptocurrency and Stock Trading
D Dasanayake, HY Dilshan… - … Conference on Big …, 2023 - ieeexplore.ieee.org
The cryptocurrency and stock markets are dynamic environments that attract traders,
seeking to enhance their investment returns. In cryptocurrency trading, there is a pullback in …
seeking to enhance their investment returns. In cryptocurrency trading, there is a pullback in …