Pinned Repositories
Activity_Monitoring
cs 2015 project
aistore
AIStore: scalable storage for AI applications
Collaborative-Filtering
The increasing importance of the web as a medium for electronic and business transactions has served as a driving force for the development of recommender systems technology. An important catalyst in this regard is the ease with which the web enables users to provide feedback about their likes or dislikes. The basic idea of recommender systems is to utilize these user data to infer customer interests. The entity to which the recommendation is provided is referred to as the user, and the product being recommended is referred to as an item. The basic models for recommender systems works with two kinds of data: 1. User-Item interactions such as ratings 2. Attribute information about the users and items such as textual profiles or relevant keywords Models that use type 1 data are referred to as collaborative filtering methods, whereas models that use type 2 data are referred to as content based methods. In this project, we will build recommendation system using collaborative filtering methods.
Headline_Generation_NLP
Hierarchical-Deep-CNN
Reinforcement-Learning
SemanticAnalyser
This code uses antlr to build the semantic analyser for cool without NOTYPES
SimilarPapers
StackMachineInterpreter
A stack machine interpreter using COOL language
TwitterPopularityPrediction
Twitter, with its public discussion model, is a good platform to perform such analysis. With Twitter’s topic structure in mind, the problem can be stated as: knowing current (and previous) tweet activity for a hashtag, can we predict its tweet activity in the future? More specifically, can we predict if it will become more popular and if so by how much? In this project, we will try to formulate and solve an instance of such problems.
satyatumati's Repositories
satyatumati/Hierarchical-Deep-CNN
satyatumati/Headline_Generation_NLP
satyatumati/SimilarPapers
satyatumati/Collaborative-Filtering
The increasing importance of the web as a medium for electronic and business transactions has served as a driving force for the development of recommender systems technology. An important catalyst in this regard is the ease with which the web enables users to provide feedback about their likes or dislikes. The basic idea of recommender systems is to utilize these user data to infer customer interests. The entity to which the recommendation is provided is referred to as the user, and the product being recommended is referred to as an item. The basic models for recommender systems works with two kinds of data: 1. User-Item interactions such as ratings 2. Attribute information about the users and items such as textual profiles or relevant keywords Models that use type 1 data are referred to as collaborative filtering methods, whereas models that use type 2 data are referred to as content based methods. In this project, we will build recommendation system using collaborative filtering methods.
satyatumati/Activity_Monitoring
cs 2015 project
satyatumati/aistore
AIStore: scalable storage for AI applications
satyatumati/Reinforcement-Learning
satyatumati/TwitterPopularityPrediction
Twitter, with its public discussion model, is a good platform to perform such analysis. With Twitter’s topic structure in mind, the problem can be stated as: knowing current (and previous) tweet activity for a hashtag, can we predict its tweet activity in the future? More specifically, can we predict if it will become more popular and if so by how much? In this project, we will try to formulate and solve an instance of such problems.
satyatumati/BigDebug-benchmark
satyatumati/bilm-tf
Tensorflow implementation of contextualized word representations from bi-directional language models
satyatumati/Connectme
satyatumati/CoolLLVMCOmpiler
satyatumati/Graph-Algorithms
satyatumati/iith_newsapp
satyatumati/IITH_Times_android
satyatumati/iithtimestest
satyatumati/IMDB-Mining
satyatumati/KernelModules
satyatumati/Lte-Analyser
satyatumati/material-design-lite
Material Design Lite Components in HTML/CSS/JS
satyatumati/Minix-MinixScheduling
satyatumati/PlacementPortal
satyatumati/Plagod
satyatumati/Random-Graphs-and-Random-Walks
satyatumati/RegressionAnalysis
Regression analysis is a statistical procedure for estimating the relationship between a target variable and a set of potentially relevant variables. In this project, we explore basic regression models on a given dataset, along with basic techniques to handle over- fitting; namely cross-validation, and regularization. With cross-validation, we test for over-fitting, while with regularization we penalize overly complex models.
satyatumati/satyatumati.github.io
satyatumati/Social-Network-Mining
In this project, we will study the various properties of social networks. In the first part of the project, we will study an undirected social network (Facebook). In the second part of the project, we will study a directed social network (Google +).
satyatumati/the-one
The Opportunistic Network Environment simulator
satyatumati/TSatyaVasanthReddy.github.io
satyatumati/ui-repo