Ashutosh Upadhye's repositories
FriendLocator-Application-Android
Developed an Android application to present locations of friends on google maps. Used BaaS (Google Firebase) to store user and location information. REST web services were used for data access. Google Location Services API was used to access current location of the user at a certain interval. Availability of Mobile Network or any Access Point could be used to transmit coarse or fine location to the Database. JSON was used to structure the database. XML was used to develop front end android UI. Services using Firebase APIs were implemented to keep record of users tracking other user locations and vice versa.
GeeksforGeeks-problems
Hacker-Rank data structure and algorithm problems
Go-Creativity-Android-Application
Developed an E-commerce Android application for movies, videos, logos and other articles. A Google Cloud Messaging system (GCM) was used to develop a chat system between the user and the administrator of the system. Data access implemented using REST web services. Movies and logos bought could be downloaded to any desirable resolution needed. PayPal-SDK was used for processing payments and transactions were accordingly stored to the database. Shopping cart sessions were implemented and persisted to the database
Google-Home-Custom-Skills
Designing custom google home skills using node js and dialogflow(API.ai)
Machine-Learning-K-means-clustering
K-means clustering is a type of unsupervised learning, which is used when you have unlabeled data (i.e., data without defined categories or groups). The goal of this algorithm is to find groups in the data, with the number of groups represented by the variable K. The algorithm works iteratively to assign each data point to one of K groups based on the features that are provided. Data points are clustered based on feature similarity. The results of the K-means clustering algorithm are: The centroids of the K clusters, which can be used to label new data Labels for the training data (each data point is assigned to a single cluster) Rather than defining groups before looking at the data, clustering allows you to find and analyze the groups that have formed organically. The "Choosing K" section below describes how the number of groups can be determined. Each centroid of a cluster is a collection of feature values which define the resulting groups. Examining the centroid feature weights can be used to qualitatively interpret what kind of group each cluster represents.
Machine-Learning-Multiple-Linear-Regression
Image result for multiple linear regression www.stat.yale.edu Multiple linear regression is the most common form of linear regression analysis. As a predictive analysis, the multiple linear regression is used to explain the relationship between one continuous dependent variable and two or more independent variables.
Neural-Networks-Cifar-Dataset-classification
CIFAR-10 is an established computer-vision data-set used for object recognition. The CIFAR-10 data consists of 60,000 (32×32) color images in 10 classes, with 6000 images per class. -There are 50,000 training images and 10,000 test images in the official data. -A convolution neural net using three convolutional neural layers was trained for one epoch. Two convolutional layers with 32x32 filters and 3x3 kernels with one full connected layer was implemented.
Object-Classification-OpenCV
Object Classification using HIST/SIFT feature extraction algorithms for Surveillance systems using OpenCV