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rahul indoria's Projects

a3c-continuous icon a3c-continuous

Tensorflow implementation of the asynchronous advantage actor-critic (a3c) reinforcement learning algorithm for continuous action space

ai-course-assignments icon ai-course-assignments

Contains the problem statements and respective solutions from my AI course. I took this course in 5th semester and was taught by Dr. Rahul Kala

ai-toolbox icon ai-toolbox

A C++ framework for MDPs and POMDPs with Python bindings

algorithms icon algorithms

:computer: Data Structures and Algorithms in Python

artificio icon artificio

Deep Learning Computer Vision Algorithms for Real-World Use

atari_games icon atari_games

implementation of deep reinforcement learning algos

barcode_1d icon barcode_1d

Vision-based 1D barcode localization method for scale and rotation invariant

baselines icon baselines

OpenAI Baselines: high-quality implementations of reinforcement learning algorithms

binary-classification-using-svm icon binary-classification-using-svm

Binary ClassificationUsing Support Vector Machines(linear kernel) in the Breast Cancer dataset569 instances, 30 features60%/40% Train/Test set5 fold cross validationAccuracy achieved 96% andPrecision/Recall 96 %/97 %

cbm_codes_open icon cbm_codes_open

This repository contains data and codes that implement common machine learning algorithms for machinery condition monitoring task.

cppcoreguidelines icon cppcoreguidelines

The C++ Core Guidelines are a set of tried-and-true guidelines, rules, and best practices about coding in C++

cs294 icon cs294

CS 294: Deep Reinforcement Learning, Fall 2017

d2l-en icon d2l-en

Interactive deep learning book with code, math, and discussions. Available in multi-frameworks.

ddpg icon ddpg

Reimplementation of DDPG(Continuous Control with Deep Reinforcement Learning) based on OpenAI Gym + Tensorflow

ddpg-1 icon ddpg-1

Implementation of DDPG (Modified from the work of Patrick Emami) - Tensorflow (no TFLearn dependency), Ornstein Uhlenbeck noise function, reward discounting, works on discrete & continuous action spaces

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