pourya-ir Goto Github PK

activity

This project uses spectrograms and scaleograms to predict human activity from sensor information.

apricot

apricot implements submodular optimization for the purpose of selecting subsets of massive data sets to train machine learning models quickly. See the documentation page: https://apricot-select.readthedocs.io/en/latest/index.html

autoxgb

XGBoost + Optuna

awesome-causality-algorithms

An index of algorithms for learning causality with data

awesome-nlp

:book: A curated list of resources dedicated to Natural Language Processing (NLP)

aws-eks-kubernetes-masterclass

AWS EKS Kubernetes - Masterclass | DevOps, Microservices

causal_inference_python_code

Python code for part 2 of the book Causal Inference: What If, by Miguel Hernán and James Robins

cs224u

Code for Stanford CS224u

cs228_pgm

🌀 Stanford CS 228 - Probabilistic Graphical Models

dash-vtk

Bringing vtk.js into Dash and Python

data-science-interview-question-bank-day1-day30-ineuron

Data Science interview question by iNeuron

deep-learning

docker-fundamentals

Docker Fundamentals

feature_engineering

kubernetes-fundamentals

Kubernetes Fundamentals

medium

missing-data-workshop

Matt Brems' Missing Data Workshop

odsc2021_nlp

Natural Language Processing Workshop at ODSC West 2021

odsc_mlops_from_model_to_prod

Machine Learning Operations (MLOps) are essential to build successful Data Science use-cases. Today, ML is powering data driven use-cases that are transforming industries around the world. In order to seize and hold it's competitive advantage business needs to reduce risk therefore a new expertise rises to include data science models in operational systems. According to Gartner Research “While many organizations have experimented with AI proofs of concept, there are still major blockers to operationalizing its development. IT leaders must strive to move beyond the POC to ensure that more projects get to production and that they do so at scale to deliver business value. (July 2020)”. In this session, we will discuss the role of MLOps and how they can help data science models from deployment to maintenance with focus on: keep track of performance degradation overtime from model predictions quality, setting up continuous evaluation metrics and tuning the model performance in both training and serving pipelines that are deployed in production.

pdp

presentation-at-udacity

python-data-viz-workshop

A 3-hour workshop on data visualization in Python with notebooks and exercises for following along.

pytorch-gnn-tutorial-odsc2021

Repository for GNN tutorial using Pytorch and Pytorch Geometric (PyG) for ODSC 2021

sorobn

🧮 Bayesian networks in Python

stacknet

StackNet is a computational, scalable and analytical Meta modelling framework

stanford_certification

testrepo

trustworthyai

trustworthy AI related projects

udacity-projects