Pinned Repositories
CV
Distributed_System_Group_Messenger2
Distributed Systems Group Messenger with Total and FIFO Ordering Guarantees
Distributed_Systems_ChatApp
Simple chat messenger
Distributed_Systems_GroupMessenger1
The goal of this app is simple: enabling two Android devices to send messages to each other.
Distributed_Systems_SimpleDht
Simple DHT Chord
Distributed_Systems_SimpleDynamo
Dimple Dynamo
Information-Retrievel-Boolean-Query-and-Inverted-Index
In this project, we were be given a sample input text file consisting of Doc IDs and sentences. Based on this provided input text file, our task was to build our own inverted index using the information extracted from the given data. Our index should be stored as a Linked List in memory as the examples shown in textbook. Having built this index, now you are required to implement a Document-at-a-time (DAAT) strategy to return Boolean query results. Finally, we are required to calculate a TF_IDF score to rank and sort the query results. Our implementation is purely based on Python3 only.
kudu
Kudu is the engine behind git/hg deployments, WebJobs, and various other features in Azure Web Sites. It can also run outside of Azure.
Machine-Learning-Algorithms
Implemented Machine Learning like Random Forest, PCA, KNN and Kmeans from scratch and using inbuilt libraries.
Machine-Learning-Logistic-Regression
This project performs classification using machine learning. It aims at the implementation of Linear Regression and Logistic Regression from the basic level. The dataset used is Wisconsin Diagnostic Breast Cancer (wdbc.dataset). Fine-needle aspiration (FNA) is a diagnostic procedure used to investigate lumps or masses. We use FNA to classify cells of breast mass as Benign (class 0) or Malignant (class 1) using logistic regression as the classifier.
jaindeepali20's Repositories
jaindeepali20/CV
jaindeepali20/Distributed_System_Group_Messenger2
Distributed Systems Group Messenger with Total and FIFO Ordering Guarantees
jaindeepali20/Distributed_Systems_ChatApp
Simple chat messenger
jaindeepali20/Distributed_Systems_GroupMessenger1
The goal of this app is simple: enabling two Android devices to send messages to each other.
jaindeepali20/Distributed_Systems_SimpleDht
Simple DHT Chord
jaindeepali20/Distributed_Systems_SimpleDynamo
Dimple Dynamo
jaindeepali20/Information-Retrievel-Boolean-Query-and-Inverted-Index
In this project, we were be given a sample input text file consisting of Doc IDs and sentences. Based on this provided input text file, our task was to build our own inverted index using the information extracted from the given data. Our index should be stored as a Linked List in memory as the examples shown in textbook. Having built this index, now you are required to implement a Document-at-a-time (DAAT) strategy to return Boolean query results. Finally, we are required to calculate a TF_IDF score to rank and sort the query results. Our implementation is purely based on Python3 only.
jaindeepali20/kudu
Kudu is the engine behind git/hg deployments, WebJobs, and various other features in Azure Web Sites. It can also run outside of Azure.
jaindeepali20/Machine-Learning-Algorithms
Implemented Machine Learning like Random Forest, PCA, KNN and Kmeans from scratch and using inbuilt libraries.
jaindeepali20/Machine-Learning-Logistic-Regression
This project performs classification using machine learning. It aims at the implementation of Linear Regression and Logistic Regression from the basic level. The dataset used is Wisconsin Diagnostic Breast Cancer (wdbc.dataset). Fine-needle aspiration (FNA) is a diagnostic procedure used to investigate lumps or masses. We use FNA to classify cells of breast mass as Benign (class 0) or Malignant (class 1) using logistic regression as the classifier.
jaindeepali20/Machine-Learning-Neural-Networks
This project implements neural network and convolutional neural network. The task is to carry out classification on Fashion-MNIST dataset. There are 10 classes of different types of clothing. Our task is to recognize an image and identify it as one of the ten classes. We will train classifiers using images from Zalando’s clothing article. The project is divided into three tasks. The first task is to build single layer(hidden) neural network from scratch in python. The second task would be to build a multi-layer Neural Network with open-source neural-network library, Keras. The third task would be to build Convolutional Neural Network (CNN) with open-source neural-network library, Keras. Layers in all three types of networks will be trained and tested on Fashion-MNIST dataset. The inference will consist of comparison between accuracy and loss between all three ways to classify and predict data using neural networks.
jaindeepali20/movie-genre-prediction
The objective of this project is to implement a movie genre prediction model using Apache Spark.
jaindeepali20/powerup-android
PowerUp is an educational choose-your-own-adventure game that utilizes a users uploaded curriculum to empower pre-adolescents to take charge of their reproductive health. This is the Android version of the game.