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nairakhilsukuma's Projects

cmu11785-deep-learning icon cmu11785-deep-learning

Projects on AI topics like speech recognition, face recognition, and neural machine translation + Projects on engineering my own version of PyTorch libraries and optimization methods from scratch.

cvxopt icon cvxopt

CVXOPT -- Python Software for Convex Optimization

dwsim icon dwsim

DWSIM is a Steady-State and Dynamic Sequential Modular Chemical Process Simulator for Windows, Linux and macOS.

examples-pse icon examples-pse

Example Python code, Jupyter Notebooks, and other files for the IDAES PSE

f21-06623 icon f21-06623

Fall 2021 Mathematical Modeling of Chemical Engineering Processes

fairseq icon fairseq

Facebook AI Research Sequence-to-Sequence Toolkit written in Python.

gdplib icon gdplib

An open library of Generalized Disjunctive Programming (GDP) models

idaes-pse icon idaes-pse

The IDAES Process Systems Engineering Framework

model-predictive-control icon model-predictive-control

Model Predictive control is used in Industries across varied processes for ensuring control of target variables while manipulating inputs. We try to explore different paradigms of such control infrastructures to add stability and improve convergence on a multivariable system

model-predictive-control-accontrol icon model-predictive-control-accontrol

This project is to use Model Predictive Control (MPC) to drive a car in a game simulator. The server provides reference waypoints (yellow line in the demo video) via websocket, and we use MPC to compute steering and throttle commands to drive the car. The solution must be robust to 100ms latency, since it might encounter in real-world application.

quipml icon quipml

Quantum Integer Programming and Machine Learning Course Website. 18-819F 47-779/785 CMU Fall 2021

resources icon resources

Best resources to learn ML, DL, Aptitude, Probability, Statistics etc.

superstructure icon superstructure

This is a model formulation to optimize the structure of a carbon capture system. The main objective of the model is to minimize the total cost, which includes capital, operating, maintenance, utilities, and power costs to achieve at least a 90% CO2 capture target.

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