Deep Learning Projects 🚀
A collection of deep learning projects implemented in TensorFlow and PyTorch, covering computer vision, healthcare applications, customer analytics, and classic datasets. Each notebook is self-contained with code, experiments, and results.
📂 Projects
- Customer Churn Prediction
File: Customer_churn_model.ipynb
Description: Built a deep learning model to predict customer churn based on historical behavioral and transactional data. Includes feature preprocessing, model training, and performance evaluation.
Key Concepts: Classification, Tabular Data, Customer Analytics
- ECG-based Heart Disease Detection
File: ECG_Based_Heart_Disease_Detection.ipynb
Description: Designed a CNN-based model to analyze ECG signals and detect potential heart disease. Demonstrates how deep learning can support healthcare diagnostics.
Key Concepts: Healthcare AI, CNNs, Time-Series Data
- Skin Cancer Classification using MobileNetV2 & TensorFlow Lite
File: Skin_Cancer_Classification_using_MobileNetV2_&_TensorFlow_Lite.ipynb
Description: Trained a lightweight MobileNetV2 model for skin lesion classification and converted it to TensorFlow Lite for deployment on mobile/edge devices.
Key Concepts: Transfer Learning, Mobile AI, Edge Deployment
- Hand Gesture Recognition
File: cnn-based-hand-gesture-recognition-model (1).ipynb
Description: Implemented a CNN to recognize hand gestures from images, enabling applications in HCI (Human-Computer Interaction).
Key Concepts: CNNs, Image Classification, Gesture Recognition
- MNIST Digit Classification
File: mnist_classification.ipynb
Description: A foundational CNN model trained on the MNIST dataset to classify handwritten digits. Serves as a baseline project for experimenting with deep learning techniques.
Key Concepts: CNNs, Image Classification, Benchmark Dataset