Tensorfow and Convolutional Neural Network, KPR Institute Engineering and Technology, Autonomous Engineering Institution, Coimbatore, India

A one-credit course on TensorFlow and Convolutional Neural Networks (CNNs) provides students with a foundational understanding of deep learning techniques for image processing and pattern recognition. By the end of the course, learners will be able to build, train, and evaluate CNN models using TensorFlow, a widely used deep learning framework.

Key outcomes include:

1. Understanding CNN Architecture – Students will grasp concepts like convolutional layers, pooling layers, activation functions, and fully connected layers.

2. Hands-on TensorFlow Implementation – Learners will gain practical experience in using TensorFlow and Keras to implement CNNs for image classification and object detection tasks.

3. Hyperparameter Tuning & Optimization – Participants will learn how to fine-tune models using techniques like dropout, batch normalization, and learning rate scheduling.

4. Model Evaluation & Interpretation – Students will analyze model performance using accuracy, loss curves, confusion matrices, and Grad-CAM visualization.

5. Real-World Applications – Learners will explore applications of CNNs in fields such as medical imaging, autonomous driving, and facial recognition.