Parth Kalkar's Projects
Implementation of a genetic algorithm to develop some filter for a picture
In this project, I will make an analog clock using openCV
Data Analysis project about analysis Google play store apps
In this project, I created a convolutional neural network which predicts the different species of a bird.
This repository contains code for applying different machine learning models on the black friday dataset
In this notebook, we're going to go through a machine learning project with the goald of predicting the sale price of bulldozers. This involves a time series data which was taken from Kaggle Bluebook Bulldozers competition.
Solve several tasks on classification and clustering.
This repository is for code and notes of data structures and algorithms practiced for coding interviews
Image Colorization is an interesting gan project to work on. We all have some old photographs and reels which were shot in the times when colored filmography was a talk of the future. Wouldn’t it be amazing if you can colorize those black and white images bringing them back to life? This repository talks about that!
Assignments of the course control theory taught at Innopolis University, Spring 2021
In this project, I worked on detecting and counting vehicles in a given image or a video.
Implementation of AI assignment. It has 2 variants Backtracking searching based on DFS and A* algorithm based on DFS.
This repository contains code for finding if a customer will generate revenue or not
Tasks solved during the data mining course at Innopolis University
Assignments of DSA - Spring 20 course, Innopolis University
Databases labs - Spring 2021
Implementation of Numerical methods to solve the differential equation and to compare the errors obtained to check the precision of that method.
This repository has deep learning material
Resume
Solutions of assignments solved during the Digital Signal Processing course taken during Spring 2022 at Innopolis University
Hackathon conducted to solve the task of showing number of accidents and other important features for road safety analysis
Lab exercises of Distributed and Network Programming course taken in Fall 2021 at Innopolis University
In this project, we will see how to use Keras and Tensorflow to build train, and test a Convolutionnal Neural Network capable of identifying the breed of a dog in a custom image. This is a supervised learning problem, specifically a multiclass classification problem.
An end to end dog classification project by deep learning and transfer learning using tensorflow 2.x
This repository consists of the code samples, assignments, and the curriculum for the Community Classroom complete Data Structures & Algorithms Java bootcamp.
This repository contains code for detecting faces using the HAAR cascade frontal face algorithm
In this project, I extracted faces of human beings from a given image and then replace the face with other image.
In this notebook, we will build a face recognition system. Many of the ideas presented here are from FaceNet and DeepFace. Face recognition problems commonly fall into two categories: Face Verification - "is this the claimed person?". For example, at some airports, you can pass through customs by letting a system scan your passport and then verifying that you (the person carrying the passport) are the correct person. A mobile phone that unlocks using your face is also using face verification. This is a 1:1 matching problem. Face Recognition - "who is this person?". For example, the video lecture showed a face recognition video (https://www.youtube.com/watch?v=wr4rx0Spihs) of Baidu employees entering the office without needing to otherwise identify themselves. This is a 1:K matching problem. FaceNet learns a neural network that encodes a face image into a vector of 512 numbers. By comparing two such vectors, you can then determine if two pictures are of the same person.
This notebook shows code for image classification on the MNIST Fashion dataset using ANN. The main objective here is to compare the performance difference between GPU and CPU runtime. The whole project was ran using Google Colab.
Implementation of a feedforward neural network from sctrach to solve basic logic gates in python