Pinned Repositories
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
Medium
pourya-ir's Repositories
pourya-ir/Medium
pourya-ir/awesome-nlp
:book: A curated list of resources dedicated to Natural Language Processing (NLP)
pourya-ir/activity
This project uses spectrograms and scaleograms to predict human activity from sensor information.
pourya-ir/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
pourya-ir/autoxgb
XGBoost + Optuna
pourya-ir/awesome-causality-algorithms
An index of algorithms for learning causality with data
pourya-ir/aws-eks-kubernetes-masterclass
AWS EKS Kubernetes - Masterclass | DevOps, Microservices
pourya-ir/causal_inference_python_code
Python code for part 2 of the book Causal Inference: What If, by Miguel Hernán and James Robins
pourya-ir/cs224u
Code for Stanford CS224u
pourya-ir/CS228_PGM
🌀 Stanford CS 228 - Probabilistic Graphical Models
pourya-ir/dash-vtk
Bringing vtk.js into Dash and Python
pourya-ir/Data-Science-Interview-Question-Bank-Day1-Day30-iNeuron
Data Science interview question by iNeuron
pourya-ir/Deep-Learning
pourya-ir/docker-fundamentals
Docker Fundamentals
pourya-ir/feature_engineering
pourya-ir/kubernetes-fundamentals
Kubernetes Fundamentals
pourya-ir/missing-data-workshop
Matt Brems' Missing Data Workshop
pourya-ir/ODSC2021_NLP
Natural Language Processing Workshop at ODSC West 2021
pourya-ir/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.
pourya-ir/PDP
pourya-ir/Presentation-At-Udacity
pourya-ir/python-data-viz-workshop
A 3-hour workshop on data visualization in Python with notebooks and exercises for following along.
pourya-ir/pytorch-gnn-tutorial-odsc2021
Repository for GNN tutorial using Pytorch and Pytorch Geometric (PyG) for ODSC 2021
pourya-ir/sorobn
🧮 Bayesian networks in Python
pourya-ir/StackNet
StackNet is a computational, scalable and analytical Meta modelling framework
pourya-ir/stanford_certification
pourya-ir/testrepo
pourya-ir/trustworthyAI
trustworthy AI related projects
pourya-ir/Udacity-Projects