Welcome to our AI Projects section, where innovation meets intelligence!
Gemini AI Nutritionist uses deep learning and algorithms to provide personalized food recommendations, empowering customers to optimize their nutrition and achieve wellness goals.
This project explores the use of advanced language models like GPT-3.5-turbo and GPT-4 in chatbot creation, providing a step-by-step guide and code examples, and highlighting the future of conversational bot technology.
Our project's goal is to create a machine learning model that can correctly predict how much healthcare will cost. This will help insurance companies set accurate rates, which will increase their profits.
This project shows how analytics and AI increase profit and reduce risk in player selection by using linear regression to predict performance for British Premier League football stars.
This project teaches beginners about Convolutional Neural Networks (CNN), their role in visual data analysis, and real-time predictions, empowering them to confidently classify images.
This project teaches Deep Neural Networks (DNNs) using a dataset of 86,000 businesses. Participants will learn key concepts and use Python libraries like pandas, numpy, and TensorFlow for data analysis, cleaning, model building, and tuning.
Diffusers and stable diffusion models can be used to improve image production. This project enables realistic synthesis with advanced deep learning techniques, interactive image creation via Gradio UI, and customizable training.
Question Answering system built on Pegasus+SQuAD for accurate responses. Optimized for high accuracy and user experience across applications
The Semantic Search System with Transformers and Faiss vectors can speed up and improve the accuracy of your searches. Find out about advanced information retrieval and personalized suggestions for a wide range of businesses.
Advanced transformer models and tokenization methods can be used to automate the summarization of documents. Quickly make high-quality abstracts to help people find knowledge and make decisions.
The goal of this project is to create a customer support chatbot by utilizing cutting-edge methods for natural language processing.
YOLOv8 is used in this project to identify human poses in real time. As the COCO dataset is used to train the model, its performance is checked, and poses in photos and videos are predicted. Pose recognition compresses the video output so that it can be s
YOLOv8 and OCR models are used for accurate and quick results in automated license plate identification and recognition.
You can improve UNet training by using checkpoints, LR adjustments, label encoding, and seeing examples to make sure they work.
