Machine Learning Problems
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Updated
Nov 6, 2021 - Jupyter Notebook
Machine Learning Problems
Interactive exploration of logistic regression, multinomial classification, and transfer learning using Python and Jupyter Notebooks in the context of data science education.
OpenAI Gym's Cartpole environment REINFORCE algorithm implementation
Gradient ascent and simulated annealing optimization algorithms for multivariate Gaussian space from scratch.
Open AI Cartpole environment gradient ascent
Python project for the Fundamentals of of Data Science class for the MSc. in Data Science at the Sapienza University of Rome. The main purpose of the project is exploring Logistic Regression & Multinomial Regression concepts along with training classifiers using Gradient Descent/Ascent.
CLIP guiding self towards an image, from text prompt
Machine learning projects
2nd assignment of the Fundamentals of Data Science exam, taught by Prof. Fabio Galasso at Sapienza University of Rome in A.Y. 2022/23
Base R Implementation of Logistic Regression from Scratch with Regularization, Laplace Approximation and more
Computational Studies of Adja Magatte Fall Internship
A simple heuristic optimizer.
Image classifier which classifies MNIST database of handwritten digits 0-9 using 28x28 pixel images
Simple example notebooks using PyTorch
This repository hosts the programming exercises for the course "Machine Learning" of AUEB Informatics.
Repository for machine learning problems implemented in python
A user-friendly web application built with Streamlit that offers personalized movie recommendations based on user ratings using a baseline predictive model and RBM neural network
Get CLIP ViT text tokens about an image, visualize attention as a heatmap.
Numerical Optimization using "hill climbing" (aka Gradient Ascent)
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