KNITPhoenix

Dimensionality-reduction-Visualization-and-Clustering-with-different-metrics

DQN-and-Duelling-DQN-implementation-on-grid-CartPole-v1-and-Acrobot-v1-environment

Evaluating-IR-models-to-optimize-performance

The goal of this project is to implement various IR models, evaluate the IR system and improve the search result based on your understanding of the models, the implementation and the evaluation.

Laser-Based-Perception-and-Navigation-with-Obstacle-Avoidance

The objective of this project is to perform perception using a laser range finder, and use the perceived information to avoid obstacles and navigate to a given destination.

Panorama-construction-using-image-stitching

Sign-Language-recognition-using-CNN

Bone-cancer-detection-using-Markov-Random-Field-Segmentation

MATLAB based project for Bone cancer detection by tumor identification using Markov Random Field Segmentation.

Ridgeformer

Ridgeformer submission for ICASSP 2025

GSM-based-Intruder-Alert-System

A System designed to send an SMS to a particular number as “Intruder Alert” or any predefined message,when the door of a room is opened forcefully with the help of IR Sensors,GSM Module(simcom 900A Module) and Microcontroller.

Handwriting-recognition-using-Neural-Networks

We will implement the backpropagation algorithm for neural networks and apply it to the task of hand-written digit recognition.

KNITPhoenix/2025-AI-College-Jobs

2025 AI/ML internship & new graduate job list updated daily

KNITPhoenix/spandey8.github.io

My personal website

KNITPhoenix/Ridgeformer

Ridgeformer submission for ICASSP 2025

KNITPhoenix/research-ms-loss

MS-Loss: Multi-Similarity Loss for Deep Metric Learning

KNITPhoenix/ContactlessFingerprintMobileApp

UB research internship

KNITPhoenix/awesome-multimodal-in-medical-imaging

A collection of resources on applications of multi-modal learning in medical imaging.

KNITPhoenix/ML-Papers-of-the-Week

🔥Highlighting the top ML papers every week.

KNITPhoenix/ML-YouTube-Courses

📺 Discover the latest machine learning / AI courses on YouTube.

KNITPhoenix/PPO-implementation-on-various-predefined-environments

KNITPhoenix/Census-based-Multi-Agent-Deep-Reinforcement-Learning-for-Day-Trading

KNITPhoenix/DQN-and-Duelling-DQN-implementation-on-grid-CartPole-v1-and-Acrobot-v1-environment

KNITPhoenix/ML-Notebooks

:fire: Machine Learning Notebooks

KNITPhoenix/RidgeBase_Fingerprint_Camera_App

KNITPhoenix/Image-denoising-using-morphological-image-processing

KNITPhoenix/Panorama-construction-using-image-stitching

KNITPhoenix/Sign-Language-recognition-using-CNN

KNITPhoenix/Dimensionality-reduction-Visualization-and-Clustering-with-different-metrics

KNITPhoenix/Linear-regression-to-predict-number-of-upvotes-for-Reddits

KNITPhoenix/Optimizing-ML-models

KNITPhoenix/Self-sustainable-alert-device

device used to sent alert over wireless network with various safety and enhanced features

KNITPhoenix/Evaluating-IR-models-to-optimize-performance

The goal of this project is to implement various IR models, evaluate the IR system and improve the search result based on your understanding of the models, the implementation and the evaluation.

KNITPhoenix/Laser-Based-Perception-and-Navigation-with-Obstacle-Avoidance

The objective of this project is to perform perception using a laser range finder, and use the perceived information to avoid obstacles and navigate to a given destination.

KNITPhoenix/Colorizing-the-Prokudin-Gorskii-photo-collection

Implementing some basic image processing algorithms to detect features to frame alignment using MATLAB

KNITPhoenix/Calculating-rotation-matrix-of-camera

KNITPhoenix/Calculating-intrinsic-parameters-of-a-camera

Figure 2 shows an image of the checkerboard, where XY Z is the world coordinate and xy is marked as the image coordinate. The edge length of each grid on the checkerboard is 10mm in reality. Suppose one pixel of the image is equivalent to 1mm. You can calculate the projection matrix from world coordinate to image coordinate based on the 32 marked points on the checkerboard. From the projection matrix you can get the intrinsic matrix.

KNITPhoenix/High-resolution-images-scrapper

Created scrapper for scrapping high resolution images from google using selenium fetching css tag and locating src attribute of the images. It fully automates chrome web browser using chromedriver.exe. It uses requests library to download images.

KNITPhoenix/Handwriting-recognition-using-Neural-Networks

We will implement the backpropagation algorithm for neural networks and apply it to the task of hand-written digit recognition.

KNITPhoenix/Multiclass-classification-using-logistics-regression-Part-2-neural-network--Forward-Propagation-

In the previous part, we implemented multi-class logistic regression to recognize handwritten digits. However, logistic regression cannot form more complex hypotheses as it is only a linear classifier.In this part, we will implement a neural network to recognize handwritten digits using the same training set as before. The neural network will be able to represent complex models that form non-linear hypotheses. For this, we will be using parameters from a neural network that we have already trained. Our goal is to implement the feedforward propagation algorithm to use our weights for prediction.

KNITPhoenix/Multi-class-classification-using-logistic-regression-Part-1-

use logistic regression and neural networks to recognize handwritten digits (from 0 to 9). Automated handwritten digit recognition is widely used today - from recognizing zip codes (postal codes) on mail envelopes to recognizing amounts written on bank checks. We have given a data set in data.mat that contains 5000 training examples of handwritten digits. The .mat format means that that the data has been saved in a native Octave/MATLAB matrix format, instead of a text (ASCII) format like a csv-file.

KNITPhoenix/Classifier-model-using-Regularized-Logistic-Regression

Implementation of regularized logistic regression to predict whether microchips from a fabrication plant passes quality assurance (QA). During QA, each microchip goes through various tests to ensure it is functioning correctly. Suppose you are the product manager of the factory and you have the test results for some microchips on two different tests. From these two tests, you would like to determine whether the microchips should be accepted or rejected. To help you make the decision, you have a dataset of test results on past microchips, from which you can build a logistic regression model.