CristinaMarsh's Stars
huseinzol05/Stock-Prediction-Models
Gathers machine learning and deep learning models for Stock forecasting including trading bots and simulations
jivoi/awesome-ml-for-cybersecurity
:octocat: Machine Learning for Cyber Security
zhouhaoyi/Informer2020
The GitHub repository for the paper "Informer" accepted by AAAI 2021.
awslabs/gluonts
Probabilistic time series modeling in Python
jbmouret/matplotlib_for_papers
Handout for the tutorial "Creating publication-quality figures with matplotlib"
AIStream-Peelout/flow-forecast
Deep learning PyTorch library for time series forecasting, classification, and anomaly detection (originally for flood forecasting).
IBM/pytorch-seq2seq
An open source framework for seq2seq models in PyTorch.
safe-graph/graph-fraud-detection-papers
A curated list of graph-based fraud, anomaly, and outlier detection papers & resources
oliverguhr/transformer-time-series-prediction
proof of concept for a transformer-based time series prediction model
xinychen/transdim
Machine learning for transportation data imputation and prediction.
CyberZHG/keras-self-attention
Attention mechanism for processing sequential data that considers the context for each timestamp.
EvilPsyCHo/Deep-Time-Series-Prediction
Seq2Seq, Bert, Transformer, WaveNet for time series prediction.
mdipietro09/DataScience_ArtificialIntelligence_Utils
Examples of Data Science projects and Artificial Intelligence use-cases
lilianweng/transformer-tensorflow
Implementation of Transformer Model in Tensorflow
lkulowski/LSTM_encoder_decoder
Build a LSTM encoder-decoder using PyTorch to make sequence-to-sequence prediction for time series data
waico/SKAB
SKAB - Skoltech Anomaly Benchmark. Time-series data for evaluating Anomaly Detection algorithms.
charles-r-earp/autograph
A machine learning library for Rust.
ningshixian/LSTM_Attention
attention-based LSTM/Dense implemented by Keras
LenzDu/Kaggle-Competition-Favorita
5th place solution for Kaggle competition Favorita Grocery Sales Forecasting
hustcxl/SP_Lib
Signal processing method and algorithm library
datastax/graph-book
The Code Examples and Notebooks for The Practitioners Guide to Graph Data
kdgutier/esrnn_torch
hihihihiwsf/AST
Adversarial Sparse Transformer for Time Series Forecasting
taspinar/GPSMachineLearning
insarlab/MintPy-tutorial
Tutorials in Jupyter Notebook for MintPy
PsiPhiTheta/LSTM-Attention
A Comparison of LSTMs and Attention Mechanisms for Forecasting Financial Time Series
Zymrael/wattnet-fx-trading
WATTNet: Learning to Trade FX with Hierarchical Spatio-Temporal Representations of Highly Multivariate Time Series
dyq0811/EEG-Transformer-seq2seq
Modified transformer network utilizing the attention mechanism for time series or any other numerical data. 6.100 project at MIT Media Lab.
treselle-systems/customer_churn_analysis
In the customer management lifecycle, customer churn refers to a decision made by the customer about ending the business relationship. It is also referred as loss of clients or customers. Customer loyalty and customer churn always add up to 100%. If a firm has a 60% of loyalty rate, then their loss or churn rate of customers is 40%. As per 80/20 customer profitability rule, 20% of customers are generating 80% of revenue. So, it is very important to predict the users likely to churn from business relationship and the factors affecting the customer decisions. We are going to show how logistic regression model using R can be used to identify the customer churn in the telecom dataset.
diegovalsesia/speckle2void
Speckle2Void: Deep Self-Supervised SAR Despeckling with Blind-Spot Convolutional Neural Networks