tlohani31's repositories
amazon-sagemaker-examples
Example notebooks that show how to apply machine learning, deep learning and reinforcement learning in Amazon SageMaker
aws-data-wrangler
Pandas on AWS
dd-panel-blog
Code for Intro to Difference-in-Differences Blog Post
DSND_Term1
Contains files related to content and project of DSND
DSND_Term2
Contains files related to content and project of DSND Term 2
Gnu-RL
A precocial reinforcement learning solution for HVAC control
inter_workshop
Workshop materials for ODSC 2019 Interpretability Talk
introtoml
ML utils
mapping-geographic-data-in-r
Files relating to the "Mapping Geographic Data in R" workshop at ODSC Boston, 2019
ml-workshop-2-of-4
Intermediate Machine Learning with Scikit-learn, 4h interactive workshop
ml-workshop-3-of-4
Advanced Machine Learning with Scikit-learn part I
ml-workshop-4-of-4
Advanced Machine Learning with Scikit-learn part II
model_validation_tutorial
Tutorial of machine learning model validation
mpc.pytorch
A fast and differentiable model predictive control (MPC) solver for PyTorch.
Network-Analysis-Made-Simple
For PyCon, PyData, ODSC, and beyond!
ODSC-missing-data-may-18
Open Data Science Conference East, 2018: Data Science with Missing Data
odsc-west-2018-missing-data
ODSC West 2018 - Good, Fast, Cheap: How to do Data Science with Missing Data
odsc-west-2018-visualization
ODSC West 2018 - Data Visualization: From Square One to Interactivity
ODSC_Neural_Nets_11-04-17
Talk "Deep Learning From Scratch Using Python" delivered at ODSC West on November 4, 2017
openshift-ml-workflows-workshop
This is material for a tutorial that @sophwats and @willb developed about machine learning workflows and about running them on OpenShift
or-gym
Environments for OR and RL Research
pomegranate
Fast, flexible and easy to use probabilistic modelling in Python.
PyAthena
PyAthena is a Python DB API 2.0 (PEP 249) client for Amazon Athena.
Reinforcement-Learning-Explained
This repository contains the lab files for Microsoft course DAT257x: Reinforcement Learning Explained
Reinforcement_Learning_for_Traffic_Light_Control
Apply deep reinforcement learning methods including DQN, DDPG for traffic light control in simulation (discrete environment), to prove the 'Green Wave' phenomenon in intelligent traffic system.
workshop
AI and Machine Learning with Kubeflow, Amazon EKS, and SageMaker