An implementation of the parser described in "Non-Projective Dependency Parsing via Latent Heads Representation (LHR) - Matteo Grella and Simone Cangialosi (2018)" [DEPRECATED]

My Deep Learning (mdl) is a repository to keep records of the most interesting learnt examples.

PyTorch implementation of different types of autoencoders

Comparison between a linear and convolutional autoencoder.

Thesis work on Video Anomaly Detection

This repository is created as part of Neural Networks and Deep Learning course at my college. This repo contains the implementations of Neural Network and Deep Learning algorithms.

Machine Learning Algorithms from scratch

An autoencoder to classify bank transactions as fraudulent or not

Autoencoder is a type of neural network where the output layer has the same dimensionality as the input layer. In simpler words, the number of output units in the output layer is equal to the number of input units in the input layer. An autoencoder replicates the data from the input to the output in an unsupervised manner and is therefore someti…

A gentle introduction to autoencoders with examples

A collections of basic autoencoders and Generative models for chemistry

Linear Regression, Logistic Regression, Neural Networks, Convolutional Neural networks, Auto Encoders

Consists of variety of Autoencoders implementation for various applications such as denoising image, reverse image search, segmantic hair segmentation.

Anomaly Detection with Multiple Techniques using KDDCUP'99 Dataset

Noise Reduction of Images using Auto Encoders.

Created and tested various autoencoder architectures (Convolutional, Variational, Linear) to learn latent representations of SARS-Cov-2 genomes and most importantly: the latent differences between the variant genomes.