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.
Explanation of the Bias Variance Tradeoff in Machine Learning
This repository includes some detailed proofs of "Bias Variance Decomposition for KL Divergence".
Machine Learning Course [ECE 501] - Spring 2023 - University of Tehran - Dr. A. Dehaqani, Dr. Tavassolipour
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.
Bias variance experiment from Learning from Data. Problem 2.24, p. 75.
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
MDL Complexity computations and experiments from the paper "Revisiting complexity and the bias-variance tradeoff".
Adversarial Robustness through Bias Variance Decomposition: A New Perspective for Federated Learning
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 is a simple python example to demonstrate bias variance
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