卷积神经网络 (Convolutional Neural Networks)

卷积神经网络简介 (Introduction to CNNs)

卷积神经网络（CNN）是一种专门用于处理网格结构数据的深度学习架构，特别适合于图像处理任务。

Convolutional Neural Networks (CNNs) are deep learning architectures specifically designed for processing grid-like data, particularly well-suited for image processing tasks.

基本组件 (Basic Components)

卷积层 (Convolutional Layers)

卷积核与特征图 (Kernels and Feature Maps)
步长与填充 (Stride and Padding)
感受野 (Receptive Field)

池化层 (Pooling Layers)

最大池化 (Max Pooling)
平均池化 (Average Pooling)
全局池化 (Global Pooling)

激活函数 (Activation Functions)

ReLU及其变体 (ReLU and Variants)
特征激活图 (Feature Activation Maps)

CNN架构设计 (CNN Architecture Design)

经典架构 (Classic Architectures)

LeNet-5
AlexNet
VGG
ResNet

现代创新 (Modern Innovations)

残差连接 (Residual Connections)
瓶颈层 (Bottleneck Layers)
注意力机制 (Attention Mechanisms)

实践应用 (Practical Applications)

图像分类 (Image Classification)

import tensorflow as tf

# 构建CNN模型 (Build CNN model)
model = tf.keras.Sequential([
    # 卷积层 (Convolutional layers)
    tf.keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)),
    tf.keras.layers.MaxPooling2D((2, 2)),
    tf.keras.layers.Conv2D(64, (3, 3), activation='relu'),
    tf.keras.layers.MaxPooling2D((2, 2)),
    
    # 全连接层 (Fully connected layers)
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(64, activation='relu'),
    tf.keras.layers.Dropout(0.5),
    tf.keras.layers.Dense(10, activation='softmax')
])

# 编译模型 (Compile model)
model.compile(
    optimizer='adam',
    loss='sparse_categorical_crossentropy',
    metrics=['accuracy']
)

目标检测 (Object Detection)

边界框预测 (Bounding Box Prediction)
锚框设计 (Anchor Box Design)
非极大值抑制 (Non-Maximum Suppression)

图像分割 (Image Segmentation)

语义分割 (Semantic Segmentation)
实例分割 (Instance Segmentation)
U-Net架构 (U-Net Architecture)

高级技巧 (Advanced Techniques)

数据增强 (Data Augmentation)

几何变换 (Geometric Transformations)
颜色变换 (Color Transformations)
混合策略 (Mixed Strategies)

迁移学习 (Transfer Learning)

预训练模型 (Pre-trained Models)
微调策略 (Fine-tuning Strategies)
特征提取 (Feature Extraction)

实战项目 (Hands-on Project)

在下一节中，我们将实现一个完整的图像分类系统，包括数据预处理、模型训练和性能评估。

In the next section, we will implement a complete image classification system, including data preprocessing, model training, and performance evaluation.