I. PyTorch Overview (概述)#

1) Definition (定义)#

PyTorch is a deep learning framework (深度学习框架) used for building and training neural networks (神经网络).

👉 It provides:

• Tensor computation (张量计算)
• Automatic differentiation (自动求导)
• GPU acceleration (GPU加速)

2) Core Idea (核心思想)#

PyTorch uses a dynamic computation graph (动态图计算图).

👉 This means:

• The graph is built during execution (运行时构建)
• Easier debugging (更易调试)

1) Widely Used in Industry (工业应用广泛)#

PyTorch is used in:

• Computer Vision (计算机视觉)
• Natural Language Processing (自然语言处理)
• Large Language Models (大模型)

2) Easy to Use (易用性强)#

• Pythonic syntax (Python风格语法)
• Flexible design (灵活设计)

3) Strong Ecosystem (生态系统强大)#

• Integrated with libraries (库集成)
• Active community (活跃社区)

1) PyTorch vs TensorFlow#

• PyTorch:

  • Dynamic graph (动态图)
  • Easier debugging (易调试)

• TensorFlow:

  • Static graph (静态图)
  • More production tools (生产工具多)

2) PyTorch vs NumPy#

• PyTorch:

  • Supports GPU (支持GPU)
  • Automatic differentiation (自动求导)

• NumPy:

  • CPU only (仅CPU)
  • No gradients (无梯度)

1) Simple Linear Model (线性模型)#

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import torch
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import torch.nn as nn
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import torch.optim as optim
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# Define model (定义模型)
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model = nn.Linear(1, 1)
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# Loss function (损失函数)
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criterion = nn.MSELoss()
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# Optimizer (优化器)
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optimizer = optim.SGD(model.parameters(), lr=0.01)
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# Training data (训练数据)
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x = torch.tensor([[1.0], [2.0], [3.0]])
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y = torch.tensor([[2.0], [4.0], [6.0]])
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# Training loop (训练循环)
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for epoch in range(100):
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    y_pred = model(x)
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    loss = criterion(y_pred, y)
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    optimizer.zero_grad()
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    loss.backward()
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    optimizer.step()
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# Output result (输出结果)
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print("Weight:", model.weight.item())
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print("Bias:", model.bias.item())