深度学习基础 (Deep Learning Fundamentals)

什么是深度学习 (What is Deep Learning)

深度学习是机器学习的一个重要分支，它通过构建和训练深层神经网络来学习数据的层次化表示。

Deep learning is a significant branch of machine learning that learns hierarchical representations of data through building and training deep neural networks.

神经网络基础 (Neural Network Basics)

人工神经元 (Artificial Neurons)

• 结构与功能 (Structure and Function)
• 激活函数 (Activation Functions)
• 权重与偏置 (Weights and Biases)

前向传播 (Forward Propagation)

• 数学原理 (Mathematical Principles)
• 计算过程 (Computation Process)
• 实现示例 (Implementation Examples)

反向传播 (Backpropagation)

• 梯度下降 (Gradient Descent)
• 链式法则 (Chain Rule)
• 参数更新 (Parameter Updates)

深度神经网络架构 (Deep Neural Network Architectures)

多层感知机 (Multilayer Perceptrons)

• 网络层次 (Network Layers)
• 层间连接 (Layer Connections)
• 常见架构 (Common Architectures)

损失函数 (Loss Functions)

• 分类问题 (Classification Problems)
• 回归问题 (Regression Problems)
• 自定义损失 (Custom Loss Functions)

实践应用 (Practical Applications)

模型训练 (Model Training)

import tensorflow as tf

# 构建简单的深度神经网络 (Build a simple deep neural network)
model = tf.keras.Sequential([
    tf.keras.layers.Dense(128, activation='relu', input_shape=(784,)),
    tf.keras.layers.Dropout(0.2),
    tf.keras.layers.Dense(64, activation='relu'),
    tf.keras.layers.Dense(10, activation='softmax')
])

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

模型评估 (Model Evaluation)

• 评估指标 (Evaluation Metrics)
• 过拟合处理 (Handling Overfitting)
• 性能优化 (Performance Optimization)

进阶主题 (Advanced Topics)

• 正则化技术 (Regularization Techniques)
• 优化器选择 (Optimizer Selection)
• 超参数调优 (Hyperparameter Tuning)

实战项目 (Hands-on Project)

在下一节中，我们将通过一个实际的图像分类项目来应用这些概念。

In the next section, we will apply these concepts through a practical image classification project.