Model Training on 中文AI术语词典https://terms-en.ai-term-hub.com/zh/tags/model-training/Recent content in Model Training on 中文AI术语词典Hugozh-cnSat, 18 Jul 2026 11:44:45 +0000欠拟合https://terms-en.ai-term-hub.com/zh/terms/underfitting/Sat, 18 Jul 2026 11:37:26 +0000https://terms-en.ai-term-hub.com/zh/terms/underfitting/<h2 id="definition">Definition</h2> <p>当统计模型或机器学习算法无法准确近似将输入映射到输出的函数时，就会发生欠拟合。这通常是因为模型对于数据而言过于简单所致。</p> <h3 id="summary">Summary</h3> <p>机器学习模型未能捕捉到训练数据潜在趋势的状态。</p> <h2 id="key-concepts">Key Concepts</h2> <ul> <li>偏差-方差权衡</li> <li>模型复杂度</li> <li>训练误差</li> <li>特征工程</li> </ul> <h2 id="use-cases">Use Cases</h2> <ul> <li>诊断模型性能不佳</li> <li>调整超参数</li> <li>选择适当的算法</li> </ul> <h2 id="related-terms">Related Terms</h2> <ul> <li><a href="https://terms-en.ai-term-hub.com/en/terms/overfitting-%E8%BF%87%E6%8B%9F%E5%90%88/">Overfitting (过拟合)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/regularization-%E6%AD%A3%E5%88%99%E5%8C%96/">Regularization (正则化)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/hyperparameter-tuning-%E8%B6%85%E5%8F%82%E6%95%B0%E8%B0%83%E4%BC%98/">Hyperparameter Tuning (超参数调优)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/generalization-%E6%B3%9B%E5%8C%96/">Generalization (泛化)</a></li> </ul>优化https://terms-en.ai-term-hub.com/zh/terms/optimization/Sat, 18 Jul 2026 11:01:16 +0000https://terms-en.ai-term-hub.com/zh/terms/optimization/<h2 id="definition">Definition</h2> <p>在机器学习中，优化指的是用于调整模型参数以最小化损失函数的算法，从而提高模型性能。常见方法包括梯度下降及其变体（如随机梯度下降、Adam等）。</p> <h3 id="summary">Summary</h3> <p>通过最小化或最大化目标函数来寻找最佳解决方案参数的数学过程。</p> <h2 id="key-concepts">Key Concepts</h2> <ul> <li>损失函数最小化</li> <li>梯度下降</li> <li>学习率</li> <li>收敛</li> </ul> <h2 id="use-cases">Use Cases</h2> <ul> <li>神经网络训练</li> <li>超参数调优</li> <li>资源分配问题</li> </ul> <h2 id="related-terms">Related Terms</h2> <ul> <li><a href="https://terms-en.ai-term-hub.com/en/terms/gradient_descent-%E6%A2%AF%E5%BA%A6%E4%B8%8B%E9%99%8D/">gradient_descent (梯度下降)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/loss_function-%E6%8D%9F%E5%A4%B1%E5%87%BD%E6%95%B0/">loss_function (损失函数)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/backpropagation-%E5%8F%8D%E5%90%91%E4%BC%A0%E6%92%AD/">backpropagation (反向传播)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/hyperparameter_tuning-%E8%B6%85%E5%8F%82%E6%95%B0%E8%B0%83%E4%BC%98/">hyperparameter_tuning (超参数调优)</a></li> </ul>Dropouthttps://terms-en.ai-term-hub.com/zh/terms/dropout/Sat, 18 Jul 2026 10:59:51 +0000https://terms-en.ai-term-hub.com/zh/terms/dropout/<h2 id="definition">Definition</h2> <p>在神经网络中，Dropout 通过在每次训练步骤中临时移除随机子集的神经元来防止过拟合。这迫使网络学习在联合使用时有用的鲁棒特征。</p> <h3 id="summary">Summary</h3> <p>Dropout 是一种正则化技术，通过在训练过程中随机忽略神经元来防止过拟合。</p> <h2 id="key-concepts">Key Concepts</h2> <ul> <li>正则化</li> <li>防止过拟合</li> <li>神经网络</li> <li>随机抑制</li> </ul> <h2 id="use-cases">Use Cases</h2> <ul> <li>训练深层前馈神经网络</li> <li>提高大型语言模型的泛化能力</li> <li>减少对特定神经元路径的计算依赖</li> </ul> <h2 id="code-example">Code Example</h2> <div class="highlight"><pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;"><code class="language-python" data-lang="python"><span style="display:flex;"><span><span style="color:#f92672">import</span> torch.nn <span style="color:#66d9ef">as</span> nn </span></span><span style="display:flex;"><span>model <span style="color:#f92672">=</span> nn<span style="color:#f92672">.</span>Sequential( </span></span><span style="display:flex;"><span> nn<span style="color:#f92672">.</span>Linear(<span style="color:#ae81ff">100</span>, <span style="color:#ae81ff">50</span>), </span></span><span style="display:flex;"><span> nn<span style="color:#f92672">.</span>Dropout(<span style="color:#ae81ff">0.5</span>), </span></span><span style="display:flex;"><span> nn<span style="color:#f92672">.</span>ReLU(), </span></span><span style="display:flex;"><span> nn<span style="color:#f92672">.</span>Linear(<span style="color:#ae81ff">50</span>, <span style="color:#ae81ff">10</span>) </span></span><span style="display:flex;"><span>) </span></span></code></pre></div><h2 id="related-terms">Related Terms</h2> <ul> <li><a href="https://terms-en.ai-term-hub.com/en/terms/l2-regularization-l2%E6%AD%A3%E5%88%99%E5%8C%96-%E6%9D%83%E9%87%8D%E8%A1%B0%E5%87%8F/">L2 Regularization (L2正则化，权重衰减)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/batch-normalization-%E6%89%B9%E5%BD%92%E4%B8%80%E5%8C%96/">Batch Normalization (批归一化)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/overfitting-%E8%BF%87%E6%8B%9F%E5%90%88/">Overfitting (过拟合)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/generalization-%E6%B3%9B%E5%8C%96/">Generalization (泛化)</a></li> </ul>