skanderhn Goto Github PK

ai-platform-text-classifier-shap

Text classification demo from our Next '19 breakout session

assortment-optimization

Optimizing an assortment of products based on measures of similarity.

automl_autosklearn

Testing some Auto-ML libraries for fun

course-nlp

A Code-First Introduction to NLP course

data-science-ipython-notebooks

Continually updated data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines. https://bit.ly/data-notes

datascience.comforecastingblog

Code + Blog entry for Datascience.com about forecasting with decomposition.

dcrnn

Implementation of Diffusion Convolutional Recurrent Neural Network in Tensorflow

deep-forecast

The code of the paper 'Deep Forecast : Deep Learning-based Spatio-Temporal Forecasting", ICML Time Series Workshop 2017.

deepar

Tensorflow implementation of Amazon DeepAR

deeplearningforphilosophers

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evaluation-procedures-for-forecasting-with-spatio-temporal-data

"Evaluation procedures for forecasting with spatio-temporal data" -- ECML 2018

exponentialsmoothingpython

A simple implementation of exponential smoothing in Python, using scipy and scikit learn

forecasting101

Code for an introduction to forecasting tutorial I gave in Feb 2018 to my colleagues.

generalmlfun

Playing around with various ML problems and technologies

graph2seq

Graph2Seq is a simple code for building a graph-encoder and sequence-decoder for NLP and other AI/ML/DL tasks.

graph_convolutional_lstm

Traffic Graph Convolutional Recurrent Neural Network

graphics

grpahics

keras-seq-2-seq-signal-prediction

An implementation of a sequence to sequence neural network using an encoder-decoder

load-forecasting-resnet

short-term load forecasting with deep residual networks

lstnet-gluon

Time-series prediction with LSTNet in Apache MXNet Gluon

m4metalearning

machine-learning-workflow-with-python

This is a comprehensive ML techniques with python: Define the Problem- Specify Inputs & Outputs- Data Collection- Exploratory data analysis -Data Preprocessing- Model Design- Training- Evaluation

multistepaheadforecasting

multi-step ahead forecasting of spatio-temporal data

n-beats

Pytorch implementation of N-BEATS: Neural basis expansion analysis for interpretable time series forecasting.

n-beats-1

N-BEATS: Neural basis expansion analysis for interpretable time series forecasting.

online-retail-case

Studying Online Retail Dataset and getting insights from it

pattern_recognize

time-series-predictoin(LSTNet,SAB,Transformer...)

pr-ssm

Python implementation of the PR-SSM.

presentations

Presentations on the topic of ML that I have given to my colleagues or to outside teams.