#Hyperparameter

#OpenAccess Book "#Hyperparameter #Tuning 🔧🪛 for Machine and Deep Learning with R" is finally published 🏁😎😂💃🏽 via @bartzbeielstein #optimization #Rstats @BartzEva @MartinZaefferer @olafmersmann More:

⚡️ In @JirkaBorovec's 3rd @kaggle post, learn how to easily convert a notebook to a standard Python package and include a simple command-line interface (CLI) to the training script for faster #hyperparameter interaction. 🤓 check it out:

Machine Learning to Predict the Antimicrobial Activity of Cold Atmospheric Plasma-Activated Liquids by Mehmet Akif Ozdemir et al. #Hyperparameter #Classifier

'Investigating the Histogram Loss in Regression', by Ehsan Imani, Kai Luedemann, Sam Scholnick-Hughes, Esraa Elelimy, Martha White. #prediction #classification #hyperparameter

I teach ridge regression in my #MachineLearning course as a first introduction to #hyperparameter tuning & model #regularization. In some cases linear regression is too complicated! My students were interested in how regularization changes the loss function, I so I made this #Python interactive dashboard with @matplotlib! Change lambda & watch the model go from OLS to global mean (all slopes = 0.0)!

Sherpa: Robust Hyperparameter Optimization for Machine Learning by Lars Hertel et al. #MachineLearning #Hyperparameter

Batch size-invariance for policy optimization by Jacob Hilton et al. #StochasticGradientDescent #Hyperparameter

#Regularization of linear regression with an L1 norm, that's LASSO regression #MachineLearning! Curious what that looks like? See the L2 ordinary least squares / data match loss (left), L1 regularization loss (center), and loss as the #hyperparameter λ increases (right). Try my interactive #Python dashboard with @matplotlib @ ∀. #DataScience

Hyperparameter Tuning in R. Learn how to tune your model's hyperparameters to get the best predictive results #rstats #hyperparameter #tuning

In my #MachineLearning course, I teach linear, Ridge and LASSO regression, to introduce: 1. hyperparameter tuning 2. L1 vs. L2 norms and to help my students truly understand these concepts I made this interactive #Python dashboard, vary the #hyperparameter and visualization the training error and regularization trade off on #GitHub @ ∀.

Responsive Safety in Reinforcement Learning by PID Lagrangian Methods by Adam Stooke et al. #Hyperparameter #ReinforcementLearning

Fantastic Generalization Measures and Where to Find Them by Yiding Jiang et al. including @bneyshabur #Hyperparameter #ComputerScience

📚 @JirkaBorovec's latest @kaggle series post presents a #hyperparameter sweep search using the @gridai_ platform, allowing parallelization over multiple spot instances and live performance observation. ⚡️ Learn more in post 4 of this 5-part series:

Neural Computed Tomography by Kunal Gupta et al. including @btcolvert #Hyperparameter #Statistics

Hyperparameter Optimization in Neural Networks via Structured Sparse Recovery by Minsu Cho et al. #NeuralNetwork #Hyperparameter

Graph Barlow Twins: A self-supervised representation learning framework for graphs by Piotr Bielak et al. including @TKajdanowicz #Hyperparameter #NeuralNetwork

Generative Adversarial Neural Operators by Md Ashiqur Rahman et al. including @AnimaAnandkumar #GenerativeAdversarialNetwork #Hyperparameter

Hyperparameter Tuning using Scikit-Learn GitHub link: 𝐍𝐨𝐭𝐞: For Halving Techniques use: from 𝐬𝐤𝐥𝐞𝐚𝐫𝐧.𝐞𝐱𝐩𝐞𝐫𝐢𝐦𝐞𝐧𝐭𝐚𝐥 import 𝐞𝐧𝐚𝐛𝐥𝐞_𝐡𝐚𝐥𝐯𝐢𝐧𝐠_𝐬𝐞𝐚𝐫𝐜𝐡_𝐜𝐯 #dataanalysis #datascience #Hyperparameter #machinelearning

RENs: Relevance Encoding Networks by @iyerkrithika21 et al. #Autoencoder #Hyperparameter

Towards Understanding Grokking: An Effective Theory of Representation Learning by Ziming Liu et al. including @RetroFlux_CN #DeepLearning #Hyperparameter