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Projects in Awesome Lists by keshav479
A curated list of projects in awesome lists by keshav479 .
https://github.com/keshav479/image-classification-using-cnn
We utilize Convolutional Neural Networks (CNNs) to classify images of cats and dogs, addressing a fundamental challenge in computer vision. We assemble a diverse dataset and preprocess the images for effective model training. Our CNN architecture, designed with convolutional and fully connected layers, extracts features crucial for classification.
Last synced: 12 Nov 2024
https://github.com/keshav479/ai-chatbot
🤖 A powerful AI-driven chatbot built using the OpenAI API, with a React and TypeScript frontend styled with Material UI. The backend is powered by Express.js and Node.js, utilizing MongoDB for data storage and JWT for secure user authentication.
cookies expressjs jwt-authentication material-ui mongodb nodejs react-typescript
Last synced: 12 Nov 2024
https://github.com/keshav479/keshav-portfolio
Welcome to my portfolio! Explore my work as a software engineer, including full-stack web development, machine learning, and AI solutions. You'll find projects showcasing my skills in the MERN stack, Python, React.js, Node.js, and Azure Machine Learning. Discover how I tackle complex problems with innovative and efficient solutions.
Last synced: 12 Nov 2024
https://github.com/keshav479/bookstore
📚 A dynamic bookstore website built with React and JavaScript, offering a seamless browsing experience for users to explore and purchase books. Features include interactive book listings, search functionality, and a user-friendly interface.
Last synced: 12 Nov 2024
https://github.com/keshav479/to-predict-the-price-of-house-depending-upon-the-size-of-house-using-linear-regression
The project aims to develop a house price prediction model using Linear Regression leveraging the relationship between the size of a house and its corresponding price
Last synced: 12 Nov 2024
https://github.com/keshav479/realtime_emotion_detection
The real-time emotion detection system built using TensorFlow and Convolutional Neural Networks (CNN). The model was trained on a dataset of images to classify human emotions and achieves an accuracy of 56%. It processes live video feeds to dynamically detect and categorise facial expressions in real time
convolutional-neural-networks jupyter-notebook kaggle python tensorflow
Last synced: 11 Oct 2024