Exercises of my CST Part III Probabilistic Machine Learning (LE49) module
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Updated
Dec 28, 2021 - Jupyter Notebook
Exercises of my CST Part III Probabilistic Machine Learning (LE49) module
Personal implementation of a simple VAE in PyTorch as described in "Auto-Encoding Variational Bayes" [Kingma, Welling, 2014]
Investigative project for my CST Part III Probabilistic Machine Learning (LE49) module
Presentation about Autoencoders for Seoul AI Meetup on July 8, 2017.
Using VAEs and GANs to understand how to generate images, over the CelebA dataset
🌟 Welcome to the Machine Learning and Deep Learning Projects repository! This project is a compilation of diverse and engaging projects spanning computer vision, Kaggle competitions, generative AI, and advanced techniques such as autoencoders and variational autoencoders
The project explores a range of methods, including both statistical analysis, traditional machine learning and deep learning approaches to anomaly detection a critical aspect of data science and machine learning, with a specific application to the detection of credit card fraud detection and prevention.
Variational Autoencoders simple implementation
Deep Learning homeworks (UniPD)
Genarating new MNIST images using VAE's
Research on Material Science using Neural Networks black box approach
People with pulmonary disease often have a high opacity, which makes segmentation of the lung from chest X-rays more difficult. In this study, I propose a methodology to improve the performance of the U-NET structure so that it is able to extract the features and spatial characteristics of the X-ray images of the chest region.
This GitHub repository provides a structured path for going from beginner to advanced in data science, machine learning, and specifically - generative AI.
Hierarchical Empirical Bayes Auto-Encoder
Generate classical paintings using Variational Autoencoders (VAEs).
Algorithms for inference in Gaussian Mixture Models.
Unofficial PyTorch implementation of GENESIS: Generative Scene Inference and Sampling with Object-Centric Latent Representations
TensorFlow code and LaTex for Bachelor Thesis: Understanding Variational Autoencoders' Latent Representations of Remote Sensing Images 🌍
[Pytorch] Minimal implementation of a Variational Autoencoder (VAE) with Categorical Latent variables inspired from "Categorical Reparameterization with Gumbel-Softmax".
A Tensorflow-layer API Implementation of Deep Generative Models (MNIST Examples)
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