Contents
Chapter 1: Introduction to Neural Networks
- 1.1 Overview of Neural Networks
- 1.2 Historical Perspective
- 1.3 Types of Neural Networks
- 1.4 Motivation for CNNs
Chapter 2: Basics of Convolutional Neural Networks
- 2.1 Fundamentals of Neural Networks
- 2.2 Convolutional Layers
- 2.3 Pooling Layers
- 2.4 Fully Connected Layers
- 2.5 Activation Functions
- 2.6 Training CNNs
Chapter 3: Convolutional Layers
- 3.1 Convolutional Operation
- 3.2 Filters and Kernels
- 3.3 Padding and Stride
- 3.4 Feature Maps
Chapter 4: Pooling Layers
- 4.1 Max Pooling
- 4.2 Average Pooling
- 4.3 Global Average Pooling
Chapter 5: Fully Connected Layers
- 5.1 Dense Layers
- 5.2 Role in CNNs
- 5.3 Output Layers
Chapter 6: Architectures of CNNs
- 6.1 LeNet-5
- 6.2 AlexNet
- 6.3 VGGNet
- 6.4 GoogLeNet (Inception)
- 6.5 ResNet
Chapter 7: Transfer Learning with CNNs
- 7.1 Pre-trained Models
- 7.2 Fine-tuning
- 7.3 Practical Considerations
Chapter 8: Object Detection and Localization
- 8.1 Region-based CNNs (R-CNN)
- 8.2 Faster R-CNN
- 8.3 You Only Look Once (YOLO)
- 8.4 Single Shot MultiBox Detector (SSD)
Chapter 9: Image Segmentation
- 9.1 Semantic Segmentation
- 9.2 Instance Segmentation
- 9.3 U-Net Architecture
Chapter 10: Applications of CNNs
- 10.1 Image Classification
- 10.2 Image Generation
- 10.3 Medical Imaging
- 10.4 Autonomous Vehicles
- 10.5 Natural Language Processing (NLP) and CNNs
Chapter 11: Challenges and Future Directions
- 11.1 Current Challenges
- 11.2 Recent Advancements
- 11.3 Future Trends
Chapter 12: Case Studies
- 12.1 Real-world Applications
- 12.2 Success Stories
- 12.3 Lessons Learned
Chapter 13: Conclusion
- 13.1 Summary
- 13.2 Final Thoughts
