Online-Recurrent-Extreme-Learning-Machine (OR-ELM) for time-series prediction, implemented in python

Labs and homeworks done during the Master Mathematics, Vision, Learning (MVA) at ENS Paris-Saclay.

Sequential Learning for Dance generation

Cloud-Assisted Multi-View Video Summarization using CNN and Bi-Directional LSTM

Sequential Learning App for Materials Discovery (SLAMD)

An investigation into weight importance measures in neural networks, relating to sequential learning and interpretability.

Corso Applicazioni Energetiche dei Materiali: Esercitazione ML

Repository for the publication "Minimal crystallographic descriptors of sorption properties in hypothetical MOFs and role in sequential learning optimization"

Virtual environment for Sequential Learning for Dance generation

Materials from the MVA Sequential Learning class.

Paper implementation of Sequential Learning for Multi-Channel Wireless Network Monitoring With Channel Switching Costs

Estimation System for Crops and Horticulture Production with Support Vector Regression (Internship Project)

Arabic part of speech tagging using arabic PUD dataset using bidirectioanl LSTM for sequential labeling classification

An implementation of the Hopfield network in Python. Includes a lot of additional classes, functions, and structures to test Sequential Learning, Energy, and other properties of the Hopfield Network.

An investigation into sequential learning of tasks using feed-forward networks built with Tensorflow

Recreation of the findings presented in the paper titled "Parsimonious Quantile Regression of Financial Asset Tail Dynamics via Sequential Learning"

Streamline your educational video series production with our automated system. Harnessing the power of GPTs and YouTube content, my tool efficiently crafts comprehensive learning experiences. Starting from basic concepts and advancing to complex topics, it's ideal for creating sequential, engaging educational content.

We as a Team have created an Image Restoration Model implementing Convolutional Neural Networks and utilizing the principles of Sequential Learning. This model can be used to restore deteriorated images to a better quality. The Model has a max accuracy of 90%, while the average floats around 86%.