Deep Learning Projects

Deep learning-based brain tumor detection using Mask R-CNN for accurate segmentation, aiding early diagnosis and assisting healthcare professionals.

A powerful face recognition system leveraging MTCNN for detection and InceptionResnetV1 for embedding extraction, offering reliable face matching and similarity detection.

Learn how to classify human actions from images using deep learning models like ResNet50 and InceptionV3 for security, healthcare and smart home applications.

Analyze Vanilla GAN vs. WGAN for MNIST image generation, using FID and Inception Score to evaluate and compare the quality of generated images.

Create a book recommendation system with machine learning using TF-IDF, KMeans clustering, and cosine similarity for accurate, data-driven suggestions

Multi-class text classification using RNN and LSTM for analyzing customer complaints, and providing real-world business insights and solutions.

Develop a hybrid recommendation system using collaborative and content-based filtering with LightFM for personalized product recommendations based on customer behavior.

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 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.

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 uses AI to find early signs of glaucoma in eye images. This helps doctors diagnose the disease quickly and prevent vision loss.

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 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.

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.

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,

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.