Hands-on session to implement a simple deep neural network, understanding layers, activations, and backpropagation.

Strategies for optimizing hyperparameters like learning rate, batch size, and regularization to improve model performance.

Explanation of CNNs, their architecture, and their role in image processing.

Hands-on session to build a CNN, understanding convolution layers, pooling, and fully connected layers

Breakout group activity on CIFAR dataset. Good chance for peer learning.

Step-by-step guide to creating and training a custom image classifier using a CNN.

Introduction to transfer learning, its applications, and an overview of object detection techniques.

Practical session on using the YOLO (You Only Look Once) algorithm for object detection.

Detailed guidance on training a YOLO model with a custom dataset for specific object detection tasks.

Exploring and implementing advanced models in computer vision for practical use cases.

Introduction to OpenCV, its libraries, and its importance in computer vision tasks.

Hands-on session on image pre-processing techniques and using built-in algorithms in OpenCV.

Capstone project where participants apply learned techniques in a guided project using OpenCV.