Deep Learning Projects
Explore the Future with Our AI Projects
Welcome to our AI Projects section, where innovation meets intelligence!
Image Segmentation using Mask R CNN with PyTorch
Deep learning-based brain tumor detection using Mask R-CNN for accurate segmentation, aiding early diagnosis and assisting healthcare professionals.
Build a Face Recognition System Using FaceNet in Python
A powerful face recognition system leveraging MTCNN for detection and InceptionResnetV1 for embedding extraction, offering reliable face matching and similarity detection.
Human Action Recognition Using Image Preprocessing
Learn how to classify human actions from images using deep learning models like ResNet50 and InceptionV3 for security, healthcare and smart home applications.
PyTorch Project to Build a GAN Model on MNIST Dataset
Analyze Vanilla GAN vs. WGAN for MNIST image generation, using FID and Inception Score to evaluate and compare the quality of generated images.
Build A Book Recommender System With TF-IDF And Clustering(Python)
Create a book recommendation system with machine learning using TF-IDF, KMeans clustering, and cosine similarity for accurate, data-driven suggestions
Build Multi-Class Text Classification Models with RNN and LSTM
Multi-class text classification using RNN and LSTM for analyzing customer complaints, and providing real-world business insights and solutions.
Build a Hybrid Recommender System in Python using LightFM
Develop a hybrid recommendation system using collaborative and content-based filtering with LightFM for personalized product recommendations based on customer behavior.
Vegetable classification with Parallel CNN model
This tutorial on Vegetable Classification using Machine Learing, guides you through automating the sorting process, improving quality control, and enhancing efficiency in agriculture and food industries.
Banana Leaf Disease Detection using Vision Transformer model
Banana Leaf Disease Detection leverages Deep Learning and Computer Vision to identify plant diseases early, helping farmers protect their crops, improve cultivation, and reduce losses with smarter, more sustainable farming methods.
Leaf Disease Detection Using Deep Learning
Our project uses deep learning to detect leaf diseases from images. By training models like VGG16 and EfficientNet on a robust dataset, we accurately diagnose plant conditions, aiding farmers in early disease detection and promoting healthier crops.
Glaucoma Detection Using Deep Learning
Glaucoma Detection Using Deep Learning uses AI to find early signs of glaucoma in eye images. This helps doctors diagnose the disease quickly and prevent vision loss.
Blood Cell Classification Using Deep Learning
Our Blood Cell Classification project uses CNN, EfficientNetB4, and VGG16 models to correctly sort blood cell images. This reduces up and improves the accuracy of research, which helps doctors make better decisions.
Skin Cancer Detection Using Deep Learning
Skin Cancer Detection project leverages advanced deep learning models, including CNN, DenseNet121, and EfficientNetB4, to accurately classify skin cancer images. This initiative aims to improve early diagnosis and patient outcomes.
Cervical Cancer Detection Using Deep Learning
Our Cervical Cancer Analysis project leverages the power of EfficientNetB0 to accurately classify different types of cervical cells. This initiative aims to enhance early cancer detection and improve patient outcomes through advanced AI technology.
Complete CNN Image Classification Models for Real Time Prediction
Learn to build real-time image classification models using Convolutional Neural Networks (CNN). This tutorial will guide you through creating and training models to predict insights for AI projects,
Predictive Analytics on Business License Data Using Deep Learning
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.