mswornavidhya's repositories
LPU_Python-for-ML---IP
For Semester 4
HelpMateAI_RAG_GenAI
This project aims to build an intelligent semantic search system, incorporating optimized PDF document processing, strategic vector database searches with cache implementation, and coherent answer generation for efficient information extraction from policy documents.
Academic-ML-Projects
IIIT-B learning curve assignments
Applied-Machine-Learning-Explainability-Techniques
Applied Machine Learning Explainability Techniques, published by Packt
awesome-AI-books
Some awesome AI related books and pdfs for learning and downloading, also apply some playground models for learning
awesome-python-projects
📱 ✅ Some awesome projects in python! 📱 ✅
awesome-quantum-ml
Curated list of awesome papers and resources in quantum machine learning
Emotions_AspectSentimentAnalysis
Aspect-based sentiment classification identifies emotions and their percentage in context sentences as speech/text
heroku-pycharm
Testing connection of Heroku through Pycharm
Awesome-Visual-Transformer
Collect some papers about transformer with vision. Awesome Transformer with Computer Vision (CV)
book-1
book
captum
Model interpretability and understanding for PyTorch
convolution-visualizer
Convolution visualizations
Dataset-forged-characters-detection-on-driving-licences-and-passports
Dataset of forged characters detection on id cards
ETL_Udemy
Leaning code of ETL Course
fastbook
The fastai book, published as Jupyter Notebooks
Hands-On-Explainable-AI-XAI-with-Python
Explainable AI with Python, published by Packt
learningPySpark
Code base for the Learning PySpark book (in preparation)
MWFeedParser
An Objective-C RSS / Atom Feed Parser for iOS
qiskit-hackathon
A github repository for our quantum CNN project for the quantum computing hackathon
Quantum-Deep-Learning
Recent advances in many fields have accelerated the demand for classification, regression, and detection problems from few 2D images/projections. Often, the heart of these modern techniques utilize neural networks, which can be implemented with deep learning algorithms. In our neural network architecture, we embed a dynamically programmable quantum circuit, acting as a hidden layer, to learn the correct parameters to correctly classify handwritten digits from the MNIST database. By starting small and making incremental improvements, we successfully reach a stunning ~95% accuracy on identifying previously unseen digits from 0 to 7 using this architecture!
ShopAssistant-AI
Enhancing Online Laptop Shopping Through Intelligent Chatbot Recommendations
stock-price-prediction
In this project, we implement and compare the performance of several machine learning and deep learning algorithms in predicting the US stock market.
svnx
Automatically exported from code.google.com/p/svnx
XAI_AnalysisProject
XAI Analysis for heart disease prediction
XGBoost_StockPricePredict
Attempt to use XGBoost in stock price prediction