Deep Learning project about the design and training of a model for Image Classification
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Updated
Nov 23, 2023 - Jupyter Notebook
Deep Learning project about the design and training of a model for Image Classification
Bias and Variance Tradeoff for debugging
This repository contains a generalized regression analysis problem solved from scratch, using only the Numpy library.
This repository has been created just for warm-up in machine learning and there are my simulation files of UT-ML course HWs.
Estimating the parametric complexity (minimum description length) of binary classifiers.
Machine Learning programs in R
TLDR: Generic Algorithms, Decision Trees, Value Iteration, POMDPs, Bias-Variance. Data preprocessing using statistical techniques and visualization is crucial to understand and analyze the data before utilizing them to train a machine learning model. Several fundamental techniques for preprocessing are presented here.
A python code for demonstration of Bias-Variance TradeOff concept in Machine Learning
Single Layer Perceptrons (SLPs) and Multi-Layer Perceptrons (MLPs) from scratch, only with numpy, for classification and regression. MLPs with Keras for time-series prediction.
Hyparameter Tuning for identifying the most significant variables that influence House Prices
Explanation of the Bias Variance Tradeoff in Machine Learning
Performing polynomial regression of varying degrees on data affected by white and Poisson noise, evaluating the model performance based on MSE loss and the bias-variance trade-off.
Adversarial Robustness through Bias Variance Decomposition: A New Perspective for Federated Learning
This is a simple python example to demonstrate bias variance
The Bias-Variance Tradeoff Visualization project provides an interactive tool to understand the bias-variance tradeoff in machine learning models. It visually demonstrates how different models perform on training and validation datasets, helping users grasp the concepts of overfitting and underfitting.
Bias variance experiment from Learning from Data. Problem 2.24, p. 75.
The projects are part of the graduate-level course CSE-574 : Introduction to Machine Learning [Spring 2019 @ UB_SUNY] . . . Course Instructor : Mingchen Gao (https://cse.buffalo.edu/~mgao8/)
This repository includes some detailed proofs of "Bias Variance Decomposition for KL Divergence".
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