Pytorch implementation of λOpt: Learn to Regularize Recommender Models in Finer Levels, KDD 2019
-
Updated
Jun 18, 2020 - Python
Pytorch implementation of λOpt: Learn to Regularize Recommender Models in Finer Levels, KDD 2019
This is Collection of Regularization Deep learning techniques with code and paper
This repo contains the experimentation of the most important deep learning techniques related to Regularization, Optimization and HPO while using LeNet CNN architecture as the playground.
Salary Prediction Classification:
Machine Learning End-to-End (Linear Regression) model project on MS Application Prediction
Deep Learning project about the design and training of a model for Image Classification
This is a classification model implementation using Random Forest and Logistic Regression in Python and Spark. Originally implemented via AWS EMR Clusters.
An effective way to avoid (or at least to reduce) overfitt
An online course on ML taught by Andrew Ng. Introduces algorithms from scratch including regression models, classification, Neural Networks, SVMs, K-Means clustering, and applications such as Photo OCR.
Add a description, image, and links to the regularization-hyperparameters topic page so that developers can more easily learn about it.
To associate your repository with the regularization-hyperparameters topic, visit your repo's landing page and select "manage topics."