Module #1 Introduction to Deep Learning Overview of deep learning, its applications, and importance in image classification
Module #2 Image Classification Basics Fundamentals of image classification, including types of classification problems and evaluation metrics
Module #3 Introduction to Convolutional Neural Networks (CNNs) Overview of CNNs, their architecture, and how theyre used for image classification
Module #4 Deep Learning Frameworks Introduction to popular deep learning frameworks such as TensorFlow, PyTorch, and Keras
Module #5 Data Preprocessing for Image Classification Techniques for preprocessing images, including data augmentation, normalization, and feature scaling
Module #6 Building a Simple CNN Hands-on exercise building a simple CNN using a deep learning framework
Module #7 Activation Functions and Optimization Overview of activation functions and optimization techniques used in deep learning
Module #8 Convolutional Layers In-depth look at convolutional layers, including types of convolutions and filter visualization
Module #9 Pooling Layers Overview of pooling layers, including max pooling and average pooling
Module #10 Batch Normalization and Regularization Techniques for improving model performance, including batch normalization and regularization
Module #11 Transfer Learning Using pre-trained models and fine-tuning for image classification tasks
Module #12 Object Detection Introduction to object detection, including techniques and architectures such as YOLO and SSD
Module #13 Image Segmentation Overview of image segmentation, including semantic and instance segmentation
Module #14 State-of-the-Art Models Overview of state-of-the-art models for image classification, including ResNet, Inception, and DenseNet
Module #15 Ensemble Methods Techniques for improving model performance using ensemble methods, including bagging and boosting
Module #16 Handling Class Imbalance Techniques for handling class imbalance in image classification datasets
Module #17 Model Evaluation and Selection Metrics and techniques for evaluating and selecting the best model for an image classification task
Module #18 Real-World Applications Real-world applications of deep learning for image classification, including self-driving cars and medical imaging
Module #19 Case Study:Image Classification with Deep Learning Hands-on exercise working on an image classification project using deep learning
Module #20 Common Challenges and Solutions Common challenges faced in deep learning for image classification and their solutions
Module #21 Advanced Topics in Image Classification Advanced topics, including attention mechanisms and generative models for image classification
Module #22 Explainability and Interpretability Techniques for explaining and interpreting deep learning models for image classification
Module #23 Deep Learning for Multi-Label Classification Techniques for handling multi-label classification problems using deep learning
Module #24 Deep Learning for Image Classification with Limited Data Techniques for image classification with limited data, including few-shot learning and transfer learning
Module #25 GPU Optimization and Parallelization Techniques for optimizing and parallelizing deep learning models for image classification using GPUs
Module #26 Cloud-Based Deep Learning Using cloud-based services for deep learning, including AWS, Google Colab, and Microsoft Azure
Module #27 Ethical Considerations in Image Classification Ethical considerations and biases in image classification, including fairness and transparency
Module #28 Deploying Deep Learning Models Techniques for deploying deep learning models for image classification, including model serving and API development
Module #29 Project Presentations and Feedback Students present their projects and receive feedback from instructors and peers
Module #30 Course Wrap-Up & Conclusion Planning next steps in Deep Learning for Image Classification career
