Day 1

Session 1: Introduction to Deep Learning for Computer Vision

• Overview of deep learning in computer vision
• Key concepts: neural networks, activation functions, and backpropagation
• Introduction to TensorFlow and Keras for deep learning

Session 2: Convolutional Neural Networks (CNNs) Fundamentals

• Understanding CNN architecture: convolution layers, pooling layers, and fully connected layers
• Key operations: convolution, activation (ReLU), pooling (max, average), and flattening
• Applications of CNNs in image classification, object detection, and segmentation

Session 3: Hands-on Lab: Building a Basic CNN for Image Classification

• Setting up the deep learning environment with TensorFlow and Keras
• Building and training a simple CNN on the MNIST dataset
• Evaluating model performance using accuracy, loss curves, and confusion matrices

Day 2

Session 1: Advanced CNN Architectures

• Deep CNN architectures: VGG, ResNet, Inception, and MobileNet
• Understanding skip connections, residual learning, and network depth
• Best practices for designing effective CNN architectures

Session 2: Hands-on Lab: Implementing Advanced CNN Models

• Building and training deep CNNs with TensorFlow
• Comparing different CNN architectures for performance and efficiency
• Visualizing feature maps and filters to understand model learning

Session 3: Introduction to Transfer Learning

• What is transfer learning? Benefits and applications in computer vision
• Using pre-trained models (e.g., VGG16, ResNet50) for custom image classification tasks
• Fine-tuning strategies to improve model performance on new datasets

Session 4: Hands-on Lab: Transfer Learning with Pre-trained Models

• Applying transfer learning using Keras applications
• Fine-tuning pre-trained models on custom image datasets
• Evaluating model performance and avoiding overfitting

Day 3

Session 1: Data Augmentation and Regularization Techniques

• Importance of data augmentation for model generalization
• Techniques: rotation, flipping, zooming, shifting, and brightness adjustment
• Regularization methods: dropout, L1/L2 regularization, and batch normalization

Session 2: Hands-on Lab: Data Augmentation and Regularization in Practice

• Implementing data augmentation using Keras ImageDataGenerator
• Applying dropout and batch normalization to improve model robustness
• Analyzing the impact of regularization techniques on model performance

Session 3: Object Detection with Deep Learning

• Introduction to object detection techniques: R-CNN, Fast R-CNN, Faster R-CNN, YOLO, and SSD
• Key concepts: bounding boxes, intersection over union (IoU), and anchor boxes
• Applications of object detection in real-time systems

Session 4: Hands-on Lab: Object Detection Using Pre-trained YOLO Model

• Implementing object detection with YOLO and OpenCV
• Training and evaluating object detection models on real-world datasets
• Drawing bounding boxes and labeling detected objects in images and videos

Day 4

Session 1: Model Optimization and Hyperparameter Tuning

• Importance of model optimization for performance and efficiency
• Hyperparameter tuning techniques: grid search, random search, and Bayesian optimization
• Optimizing learning rate, batch size, and architecture parameters

Session 2: Hands-on Lab: Hyperparameter Tuning with Keras Tuner

• Setting up Keras Tuner for automated hyperparameter optimization
• Experimenting with different architectures and training configurations
• Analyzing results to identify optimal model settings

Session 3: Model Deployment for Real-World Applications

• Preparing deep learning models for deployment: model serialization and exporting
• Deploying models using Flask and FastAPI for RESTful APIs
• Introduction to deploying models on cloud platforms (AWS, Azure, Google Cloud)

Session 4: Hands-on Lab: Deploying a Computer Vision Model as a Web Service

• Creating a REST API to serve image classification models
• Deploying models to cloud environments for scalable access
• Testing APIs with real-time image data

Session 5: Capstone Project and Course Wrap-Up

• Capstone project: Build, train, optimize, and deploy a deep learning model for a computer vision task
• Group presentations and feedback on project outcomes
• Key takeaways, advanced topics for further exploration, and resources for continuous learning