Collection of must read papers for Data Science, or Machine Learning / Deep Learning Engineer

Implementation of Logistic Regression, MLP, CNN, RNN & LSTM from scratch in python. Training of deep learning models for image classification, object detection, and sequence processing (including transformers implementation) in TensorFlow.

RNN architectures trained with Backpropagation and Reservoir Computing (RC) methods for forecasting high-dimensional chaotic dynamical systems.

Cuda implementation of Extended Long Short Term Memory (xLSTM) with C++ and PyTorch ports

This project provides implementations with Keras/Tensorflow of some deep learning algorithms for Multivariate Time Series Forecasting: Transformers, Recurrent neural networks (LSTM and GRU), Convolutional neural networks, Multi-layer perceptron

Time Series Analysis of Air Pollutants(PM2.5) using LSTM model

This project is about performing Speaker diarization for Hindi Language.

Deep Learning notes and practical implementation with Tensorflow and keras. Text Analytics and practical application implementation with NLTK, Spacy and Gensim.

Monorepo for trading algorithms

Natural Language Processing for Multiclass Classification: A repository containing NLP techniques for multiclass classification of text data.

Contact: Alexander Hartl, Maximilian Bachl, Fares Meghdouri. Explainability methods and Adversarial Robustness metrics for RNNs for Intrusion Detection Systems. Also contains code for "SparseIDS: Learning Packet Sampling with Reinforcement Learning" (branch "rl").

Weather forecasting using recurrent neural network

Fake News Detection Using Recurrent Neural Networks (RNNs) & Long Short Term Memory (LSTM).

This repository contains the notebooks used in my project "Air quality analysis and forecasting"

Beginner Friendly CheatCodes

In this project we will be building a model capable of generating notes and chords after learning from the dataset of songs we provide to our recurrent neural network and create songs. Before we start, let us recall few of the basic concepts and terminologies we will be using in this project and add to that knowledge the concepts required to suc…

Predicting stock market prices using RNN with LSTM

Deep Learning, Attention, Transformers, BERT, GPT-2, GTP-3

The main task of the character-level language model is to predict the next character given all previous characters in a sequence of data, i.e. generates text character by character.

Forecasting exchange rates by using commodities prices