从零开始掌握人工智能：核心原理、实战代码与未来趋势

 # 使用PyTorch实现简单CNN

    
 import torch
    
 import torch.nn as nn
    
  
    
 class CNN(nn.Module):
    
     def __init__(self):
    
     super().__init__()
    
     self.conv1 = nn.Conv2d(1, 32, 3)
    
     self.pool = nn.MaxPool2d(2, 2)
    
     self.fc1 = nn.Linear(32*13*13, 10)
    
  
    
     def forward(self, x):
    
     x = self.pool(torch.relu(self.conv1(x)))
    
     x = x.view(-1, 32*13*13)
    
     return self.fc1(x)
    
    
    
    
    
![](https://ad.itadn.com/c/weblog/blog-img/images/2025-08-18/RKkt4pnGuaPzq0NBSUbydIwXofCV.png)

 from sklearn.ensemble import GradientBoostingClassifier

    
 from imblearn.over_sampling import SMOTE
    
  
    
 # 处理样本不均衡
    
 smote = SMOTE()
    
 X_res, y_res = smote.fit_resample(X_train, y_train)
    
  
    
 # 训练GBDT模型
    
 gbdt = GradientBoostingClassifier(n_estimators=200)
    
 gbdt.fit(X_res, y_res)
    
  
    
 # 特征重要性分析
    
 pd.Series(gbdt.feature_importances_, index=X.columns).sort_values().plot.barh()
    
    
    
    
    
![](https://ad.itadn.com/c/weblog/blog-img/images/2025-08-18/6s1Kax4om3UJtQ5EiNvRAqVw8bch.png)

 # 使用conda创建环境

    
 conda create -n ai python=3.9
    
 conda activate ai
    
  
    
 # 安装核心库
    
 pip install torch torchvision --index-url https://download.pytorch.org/whl/cu118
    
 pip install tensorflow scikit-learn pandas matplotlib

 * Python编程
 * 线性代数/概率论
 * 机器学习基础

 * 深度学习理论
 * 计算机视觉/NLP
 * 分布式训练

 * 论文复现
 * 模型压缩
 * 自研算法

 # 混合精度训练

    
 scaler = torch.cuda.amp.GradScaler()
    
  
    
 with torch.cuda.amp.autocast():
    
     output = model(input)
    
     loss = criterion(output, target)
    
     
    
 scaler.scale(loss).backward()
    
 scaler.step(optimizer)
    
 scaler.update()
    
    
    
    
    
![](https://ad.itadn.com/c/weblog/blog-img/images/2025-08-18/Y5UBX3CITk0KwbiNSVxFuWy4gMp7.png)