overfitting

Notes for Deep Learning Specialization Courses led by Andrew Ng.

A new test set for ImageNet

Official implementation of the NeurIPS'2020 paper 'Self-Adaptive Training: beyond Empirical Risk Minimization'

Developer Version of the R package CAST: Caret Applications for Spatio-Temporal models

TensorFlow in Practice Specialization. Join our Deep Learning Adventures community 🎉 and become an expert in Deep Learning, TensorFlow, Computer Vision, Convolutional Neural Networks, Kaggle Challenges, Data Augmentation and Dropouts Transfer Learning, Multiclass Classifications and Overfitting and Natural Language Processing NLP as well as Time Series Forecasting 😀 All while having fun learning and participating in our Deep Learning Trivia games 🎉 http://bit.ly/deep-learning-tf

[ICLR 2021] "Robust Overfitting may be mitigated by properly learned smoothening" by Tianlong Chen*, Zhenyu Zhang*, Sijia Liu, Shiyu Chang, Zhangyang Wang

Tuning GBMs (hyperparameter tuning) and impact on out-of-sample predictions

Machine Learning to predict share prices in the Oil & Gas Industry

**Supervised-Learning** (with some Kaggle winning solutions and their reason of Model Selection for the given dataset).

A study about the following problems: what is the memorization problem in meta-learning; why does memorization problem happen; and how to prevent it. (ICLR 2020)

Deep Learning Adventures. Join our Deep Learning Adventures community 🎉 and become an expert in Deep Learning, TensorFlow, Computer Vision, Convolutional Neural Networks, Kaggle Challenges, Data Augmentation and Dropouts Transfer Learning, Multiclass Classifications and Overfitting and Natural Language Processing NLP as well as Time Series Forecasting 😀 All while having fun learning and participating in our Deep Learning Trivia games 🎉 http://bit.ly/deep-learning-tf

playing with Dwork's adaptive holdout and how to use it for a grid-search

It is from Kaggle Competitions where the training dataset is very small and the testing dataset is very large and we have to avoid or reduce overfiting by looking for best possible ways to overcome the most popular problem faced in field of predictive analytics.

Machine-Learning-Regression

Term project for CAP6619 Deep Learning class, Fall 2018 - dropout vs. batch normalization

User Modelling for Avoiding Overfitting in Interactive Knowledge Elicitation for Prediction

Xinshao Wang, Postdoc, University of Oxford.

Supplementary code for the paper "Stochastic Weight Matrix-based Regularization Methods for Deep Neural Networks" - an accepted paper of LOD2019

Metrics to assess the generalisation ability of NILM algorithms

Decision Tree classifier from scratch without any machine learning libraries

All exercises for the course Elements of AI - Building AI

A visual example of the concepts of under and overfitting in supervised machine learning using U.S. state border data.

Logistic Regression algorithm to classify two classes of 2-D data samples using 1) 2-D feature vector & 2) its mapping to a higher dimensional feature space.

Simple Demo to show how L2 Regularization avoids overfitting in Deep Learning/Neural Networks

Python version of Andrew Ng's Machine Learning Course.

Algorithmic Strategy Backtesting

Analyse OverFitting of data

Dropout: A Simple Way to Prevent Neural Networks from Overfitting

🚧 ML algorithms, employing different approaches

Fraud detection over twitter feed data