IJCAI 2021, "Comparing Kullback-Leibler Divergence and Mean Squared Error Loss in Knowledge Distillation"
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mean-squared-error
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MDL Complexity computations and experiments from the paper "Revisiting complexity and the bias-variance tradeoff".
machine-learning statistics ai information-theory linear-regression artificial-intelligence model-selection complexity mdl ridge-regression linear-regression-models linear-models minimum-description-length bias-variance-tradeoff bias-variance-trade double-descent mean-squared-error
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PyTorch implementations of the beta divergence loss.
numpy torch pytorch kullback-leibler-divergence distance-measures distance-metric nmf loss-functions loss kl-divergence divergence non-negative-matrix-factorization mean-square-error nmf-decomposition objective-functions beta-divergence distance-metrics divergences mean-squared-error itakura-saito-divergence
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- Python
Bayesian Regression of Latent Source Modelling for Predicting Price Variation of Bitcoin
bitcoin linear-regression cryptocurrency price-prediction bayesian-regression mean-squared-error latent-source-model
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This project predicts used car prices using a feedforward neural network regression model implemented in PyTorch. Features include car age, mileage, and other attributes. The pipeline supports feature normalization, train/validation/test splitting, and visualization of training and validation loss curves.
python deep-learning scikit-learn pandas pytorch batch-normalization regression-models data-normalization gradio model-evaluation he-initializer adam-optimizer model-training price-prediction relu-layer car-price-prediction train-validation-test mean-squared-error gradio-interface gradio-app
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Machine Learning for Data 3141 Reichman University Spring 2022 - 6 Homework Projects
svm linear-regression ml naive-bayes-classifier logistic-regression gradient-descent k-means polynomial-regression knn decision-tree-classifier em-algorithm multivariate-regression lagrange-multipliers unsupervised-machine-learning mle-estimation pac-learning k-fold-cross-validation mean-squared-error
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This project used various machine learning algorithms to predict rainfall.
svm linear-regression logistic-regression decision-trees jaccard-similarity knn f1-score r2-score mean-absolute-error mean-squared-error
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What Happens if We Use a Mean Squared Error Loss for Binary Classification?
machine-learning deep-learning neural-network mse loss-functions binary-crossentropy mean-squared-error crossentropy
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Intrusion Detection System for MQTT Enabled IoT.
mqtt deep-learning sklearn data-visualization feature-extraction mqtt-server data-processing mqtt-protocol binary-classification keras-tensorflow classification-report intrusion-detection-system multi-class-classification artifical-neural-network r2-score mean-absolute-error root-mean-square-error mean-squared-error
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Forecasting MCTS Variant Outcomes Across Board Games
games research monte-carlo regression artificial-intelligence kaggle-competition xgboost monte-carlo-tree-search board-games catboost code-competition lgbm mean-squared-error maastricht-university mcts-agents
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Neural Network implemented with different Activation Functions i.e, sigmoid, relu, leaky-relu, softmax and different Optimizers i.e, Gradient Descent, AdaGrad, RMSProp, Adam. You can choose different loss functions as well i.e, cross-entropy loss, hinge-loss, mean squared error (MSE)
python neural-network jupyter-notebook python3 pycharm gradient-descent loss-functions adagrad rmsprop pycharm-ide leaky-relu adam-optimizer hinge-loss cross-entropy-loss activation-functions optimizers sigmoid-activation mean-squared-error relu-activation softmax-activation
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A small code for understanding linear regression.
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CERN Electron collision data- Invariant mass prediction
tensorflow sklearn keras ml regression prediction artificial-neural-networks particle-physics gradio adam-optimizer mean-squared-error gradio-interface
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All things around ... Regression
linear-regression regression regression-models knn-regression support-vector-regression decision-tree-regression random-forest-regression xgboost-regression r2-score mean-squared-error gradient-boosting-regression
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A collection of TypeScript-based machine learning helpers.
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- TypeScript
Laptop Price Prediction using Machine Learning. This project analyzes laptop specifications like RAM, CPU, storage, and GPU to predict prices using data preprocessing, EDA, and a Linear Regression model.
numpy pandas seaborn matplotlib-pyplot train-test-split sklearn-metrics sklearn-model mean-squared-error linerregression sklearn-linear-model
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- Python
💎 'The Linear Regression Challenge: Diamonds Price Prediction' @ironhack Data Analytics Bootcamp
python machine-learning statistics numpy linear-regression sklearn pandas seaborn ironhack matplotlib-pyplot rmse-score mean-squared-error
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This repository contains a Jupyter Notebook that implements PCA (Principal Component Analysis) from scratch for facial recognition. It demonstrates the steps involved in PCA, including eigenface computation and accuracy comparisons for different components.
facial-recognition dimensionality-reduction eigenvectors principal-component-analysis eigenfaces mean-squared-error atnt-data
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Forecasting % Baseline, a measure of solar panel energy output, using weather and solar irradiance data.
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This project provided practice with logistic regression and the cost functions MSE and log loss
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- Python
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