Systematically master the most important architecture for image recognition
Series Overview
This series is a practical educational content consisting of 5 chapters that allows you to learn Convolutional Neural Networks (CNN) from fundamentals progressively.
CNN is the most important deep learning architecture for computer vision tasks such as image recognition, object detection, and segmentation. By mastering local feature extraction through convolutional layers, dimensionality reduction through pooling layers, and efficient model construction techniques through transfer learning, you can build practical image recognition systems. We provide systematic knowledge from basic CNN mechanisms to modern architectures like ResNet and EfficientNet, and object detection with YOLO.
Features:
- From Fundamentals to Applications: Systematic learning from convolution principles to object detection
- Implementation-Focused: Over 40 executable PyTorch code examples and practical techniques
- Intuitive Understanding: Understand operational principles through filter and feature map visualization
- PyTorch Full Compliance: Latest implementation methods using industry-standard framework
- Transfer Learning Practice: Efficient development methods using pre-trained models
Total Learning Time: 100-120 minutes (including code execution and exercises)
How to Learn
Recommended Learning Order
For Beginners (No CNN knowledge):
- Chapter 1 → Chapter 2 → Chapter 3 → Chapter 4 → Chapter 5 (All chapters recommended)
- Duration: 100-120 minutes
For Intermediate Learners (Deep learning experience):
- Chapter 2 → Chapter 3 → Chapter 4 → Chapter 5
- Duration: 80-90 minutes
Topic-Specific Enhancement:
- Transfer Learning: Chapter 3 (intensive study)
- Data Augmentation: Chapter 4 (intensive study)
- Object Detection: Chapter 5 (intensive study)
- Duration: 20-25 minutes per chapter
Chapter Details
Chapter 1: CNN Fundamentals and Convolutional Layers
Difficulty: Beginner to Intermediate
Reading Time: 20-25 minutes
Code Examples: 8
Learning Content
- Principles of Convolution Operations - Understanding kernels, strides, and padding
- Filters and Feature Maps - Mechanisms of edge detection and texture extraction
- Channels and Dimensions - RGB image processing and multi-channel convolution
- Convolutional Layer Implementation - Conv2D implementation and visualization with PyTorch
- Receptive Field Concept - Stacking convolutional layers and field of view expansion
Learning Objectives
- Understand the mathematical principles of convolution operations
- Explain the mechanism of feature extraction by filters
- Understand the effects of padding and stride
- Implement Conv2D with PyTorch
- Visualize and interpret feature maps
Chapter 2: Pooling Layers and CNN Architectures
Difficulty: Intermediate
Reading Time: 20-25 minutes
Code Examples: 8
Learning Content
- Role of Pooling Layers - Max Pooling, Average Pooling, and dimensionality reduction
- LeNet and AlexNet - Features and implementation of early CNN architectures
- VGGNet - Design philosophy of stacking small filters
- ResNet - Deep networks and solving gradient vanishing with residual connections
- EfficientNet - Efficient scaling methods
Learning Objectives
- Understand the roles and types of pooling layers
- Explain features of representative CNN architectures
- Understand the importance of ResNet's residual connections
- Implement VGG/ResNet with PyTorch
- Understand criteria for architecture selection
Chapter 3: Transfer Learning and Fine-Tuning
Difficulty: Intermediate
Reading Time: 20-25 minutes
Code Examples: 8
Learning Content
- Principles of Transfer Learning - Utilizing ImageNet pre-trained models
- Feature Extraction Approach - Fast learning with frozen layers
- Fine-Tuning - Gradual layer unfreezing and training
- Using timm Library - Hundreds of pre-trained models
- Domain Adaptation - Strategies for applying to different datasets
Learning Objectives
- Understand the benefits and principles of transfer learning
- Load pre-trained models with torchvision
- Distinguish between feature extraction and fine-tuning
- Utilize latest models with timm
- Design transfer learning strategies based on data size
Chapter 4: Data Augmentation and Model Optimization
Difficulty: Intermediate
Reading Time: 20-25 minutes
Code Examples: 8
Learning Content
- Basic Data Augmentation - Rotation, flipping, cropping, and color transformation
- Advanced Augmentation Methods - Mixup, CutMix, and RandAugment
- Regularization Techniques - Dropout, Batch Normalization, and Weight Decay
- Mixed Precision Training - Acceleration and memory reduction with FP16
- Learning Rate Scheduling - Cosine Annealing and Warmup
Learning Objectives
- Implement data augmentation with torchvision.transforms
- Understand the effects of Mixup/CutMix
- Apply regularization methods appropriately
- Accelerate with Mixed Precision training
- Use learning rate schedulers effectively
Chapter 5: Object Detection Introduction
Difficulty: Intermediate
Reading Time: 25-30 minutes
Code Examples: 8
Learning Content
- Object Detection Fundamentals - Bounding Box, IoU, and Non-Maximum Suppression
- YOLO Architecture - One-stage detection mechanism and implementation
- Faster R-CNN - Two-stage detection and Region Proposal Network
- Detection Evaluation Metrics - mAP and Precision-Recall curves
- Practical Object Detection - Integration with OpenCV and real-time inference
Learning Objectives
- Understand basic concepts of object detection
- Explain IoU and NMS mechanisms
- Implement object detection with YOLO
- Evaluate detection performance with mAP
- Perform real-time detection in coordination with OpenCV
Overall Learning Outcomes
Upon completing this series, you will acquire the following skills and knowledge:
Knowledge Level (Understanding)
- Explain the principles of CNN convolution operations and pooling
- Understand the features and evolution of representative CNN architectures
- Explain the mechanisms and benefits of transfer learning
- Understand the effects of data augmentation and regularization
- Explain basic object detection algorithms
Practical Skills (Doing)
- Implement CNNs with PyTorch
- Execute transfer learning with pre-trained models
- Build data augmentation pipelines
- Accelerate training with Mixed Precision
- Build object detection systems with YOLO
Application Ability (Applying)
- Select appropriate architectures for new image classification tasks
- Design transfer learning strategies based on data size
- Optimize models while preventing overfitting
- Build real-time inference systems
Prerequisites
To effectively learn this series, the following knowledge is desirable:
Required (Must Have)
- Python Fundamentals: Variables, functions, classes, loops, and conditionals
- NumPy Fundamentals: Array operations, broadcasting, and basic mathematical functions
- Deep Learning Fundamentals: Neural networks, backpropagation, and gradient descent
- PyTorch Fundamentals: Tensor operations, nn.Module, Dataset and DataLoader
- Linear Algebra Fundamentals: Matrix operations, dot products, and shape transformations
Recommended (Nice to Have)
- Image Processing Fundamentals: Pixels, channels, and image formats
- Optimization Algorithms: Adam, SGD, and learning rate scheduling
- Matplotlib/PIL: Image loading and visualization
- GPU Environment: Basic understanding of CUDA
Recommended Prior Learning:
Technologies and Tools Used
Main Libraries
- PyTorch 2.0+ - Deep learning framework
- torchvision 0.15+ - Image processing and model library
- timm 0.9+ - PyTorch Image Models, pre-trained model collection
- OpenCV 4.8+ - Image processing and object detection
- NumPy 1.24+ - Numerical computation
- Matplotlib 3.7+ - Visualization
- Pillow 10.0+ - Image loading and conversion
Development Environment
- Python 3.8+ - Programming language
- Jupyter Notebook / Lab - Interactive development environment
- Google Colab - GPU environment (free to use)
- CUDA 11.8+ / cuDNN - GPU acceleration (recommended)
Datasets
- CIFAR-10/100 - Image classification fundamentals
- ImageNet - Large-scale image classification (pre-training)
- COCO - Object detection and segmentation
Let's Get Started!
Ready to begin? Start with Chapter 1 and master CNN technology!
Chapter 1: CNN Fundamentals and Convolutional Layers →
Next Steps
After completing this series, we recommend progressing to the following topics:
Advanced Learning
- Segmentation: U-Net, Mask R-CNN, and semantic segmentation
- Vision Transformer: ViT, Swin Transformer, and attention mechanisms
- Image Generation: GAN, VAE, and Diffusion Models
- Model Optimization: Quantization, pruning, and Knowledge Distillation
Related Series
- - Attention, Transformer, and latest architectures
- - Segmentation and pose estimation
- - ONNX, TensorRT, and production deployment
Practical Projects
- Image Classification System - Classify custom datasets with transfer learning
- Face Recognition App - Real-time face detection and authentication
- Medical Image Diagnosis - Anomaly detection in X-ray images
- Autonomous Driving Simulation - Object detection and lane recognition
Update History
- 2025-10-21: v1.0 first edition released
Your CNN learning journey starts here!