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SUMMARY.html
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<div id='write' class = 'is-node'><h1><a name='header-n0' class='md-header-anchor '></a>深度学习笔记目录</h1><h2><a name='header-n2' class='md-header-anchor '></a>第一门课 神经网络和深度学习(Neural Networks and Deep Learning) </h2><p><a href='lesson1-week1.md'>第一周：深度学习引言(Introduction to Deep Learning) </a></p><p>1.1 欢迎(Welcome)</p><p>1.2 什么是神经网络？(What is a Neural Network) </p><p>1.3 神经网络的监督学习(Supervised Learning with Neural Networks) </p><p>1.4 为什么神经网络会流行？(Why is Deep Learning taking off?) </p><p>1.5 关于本课程(About this Course) </p><p>1.6 课程资源(Course Resources) </p><p>1.7 Geoffery Hinton 专访(Geoffery Hinton interview) </p><p><a href='lesson1-week2.md'>第二周：神经网络的编程基础(Basics of Neural Network programming) </a></p><p>2.1 二分类(Binary Classification) </p><p>2.2 逻辑回归(Logistic Regression) </p><p>2.3 逻辑回归的代价函数（Logistic Regression Cost Function） </p><p>2.4 梯度下降（Gradient Descent） </p><p>2.5 导数（Derivatives）</p><p>2.6 更多的导数例子（More Derivative Examples） </p><p>2.7 计算图（Computation Graph）</p><p>2.8 使用计算图求导数（Derivatives with a Computation Graph） </p><p>2.9 逻辑回归的梯度下降（Logistic Regression Gradient Descent） </p><p>2.10 梯度下降的例子(Gradient Descent on m Examples) </p><p>2.11 向量化(Vectorization) </p><p>2.12 更多的向量化例子（More Examples of Vectorization）</p><p>2.13 向量化逻辑回归(Vectorizing Logistic Regression) </p><p>2.14 向量化逻辑回归的梯度计算（Vectorizing Logistic Regression's Gradient）</p><p>2.15 Python中的广播机制（Broadcasting in Python）</p><p>2.16 关于 Python与numpy向量的使用（A note on python or numpy vectors）</p><p>2.17 Jupyter/iPython Notebooks快速入门（Quick tour of Jupyter/iPython Notebooks）</p><p>2.18 逻辑回归损失函数详解（Explanation of logistic regression cost function）</p><p><a href='lesson1-week3.md'>第三周：浅层神经网络(Shallow neural networks)</a></p><p>3.1 神经网络概述（Neural Network Overview）</p><p>3.2 神经网络的表示（Neural Network Representation） </p><p>3.3 计算一个神经网络的输出（Computing a Neural Network's output）</p><p>3.4 多样本向量化（Vectorizing across multiple examples）</p><p>3.5 向量化实现的解释（Justification for vectorized implementation）</p><p>3.6 激活函数（Activation functions） </p><p>3.7 为什么需要非线性激活函数？（why need a nonlinear activation function?） </p><p>3.8 激活函数的导数（Derivatives of activation functions） </p><p>3.9 神经网络的梯度下降（Gradient descent for neural networks） </p><p>3.10（选修）直观理解反向传播（Backpropagation intuition） </p><p>3.11 随机初始化（Random+Initialization）</p><p><a href='lesson1-week4.md'>第四周：深层神经网络(Deep Neural Networks)</a></p><p>4.1 深层神经网络（Deep L-layer neural network） </p><p>4.2 前向传播和反向传播（Forward and backward propagation） </p><p>4.3 深层网络中的前向和反向传播（Forward propagation in a Deep Network）</p><p>4.4 核对矩阵的维数（Getting your matrix dimensions right） </p><p>4.5 为什么使用深层表示？（Why deep representations?）</p><p>4.6 搭建神经网络块（Building blocks of deep neural networks）</p><p>4.7 参数VS超参数（Parameters vs Hyperparameters） </p><p>4.8 深度学习和大脑的关联性（What does this have to do with the brain?）</p><h2><a name='header-n51' class='md-header-anchor '></a>第二门课 改善深层神经网络：超参数调试、正则化以及优化(Improving Deep Neural Networks:Hyperparameter tuning, Regularization and Optimization)</h2><p><a href='lesson2-week1.md'>第一周：深度学习的实践层面(Practical aspects of Deep Learning) </a></p><p>1.1 训练，验证，测试集（Train / Dev / Test sets） </p><p>1.2 偏差，方差（Bias /Variance） </p><p>1.3 机器学习基础（Basic Recipe for Machine Learning） </p><p>1.4 正则化（Regularization）</p><p>1.5 为什么正则化有利于预防过拟合呢？（Why regularization reduces overfitting?）</p><p>1.6 dropout 正则化（Dropout Regularization）</p><p>1.7 理解 dropout（Understanding Dropout）</p><p>1.8 其他正则化方法（Other regularization methods）</p><p>1.9 标准化输入（Normalizing inputs）</p><p>1.10 梯度消失/梯度爆炸（Vanishing / Exploding gradients）</p><p>1.11 神经网络的权重初始化（Weight Initialization for Deep Networks） </p><p>1.12 梯度的数值逼近（Numerical approximation of gradients）</p><p>1.13 梯度检验（Gradient checking）</p><p>1.14 梯度检验应用的注意事项（Gradient Checking Implementation Notes） </p><p><a href='lesson2-week2.md'>第二周：优化算法 (Optimization algorithms) </a></p><p>2.1 Mini-batch 梯度下降（Mini-batch gradient descent） </p><p>2.2 理解Mini-batch 梯度下降（Understanding Mini-batch gradient descent）</p><p>2.3 指数加权平均（Exponentially weighted averages）</p><p>2.4 理解指数加权平均（Understanding Exponentially weighted averages） </p><p>2.5 指数加权平均的偏差修正（Bias correction in exponentially weighted averages）</p><p>2.6 momentum梯度下降（Gradient descent with momentum）</p><p>2.7 RMSprop——root mean square prop（RMSprop）</p><p>2.8 Adam优化算法（Adam optimization algorithm）</p><p>2.9 学习率衰减（Learning rate decay）</p><p>2.10 局部最优问题（The problem of local optima）</p><p><a href='lesson2-week3.md'>第三周超参数调试，batch正则化和程序框架（Hyperparameter tuning, Batch Normalization and Programming Frameworks)</a> </p><p>3.1 调试处理（Tuning process） </p><p>3.2 为超参数选择和适合范围（Using an appropriate scale to pick hyperparameters）</p><p>3.3 超参数调试实践：Pandas vs. Caviar（Hyperparameters tuning in practice: Pandas vs. Caviar）</p><p>3.4 网络中的正则化激活函数（Normalizing activations in a network） </p><p>3.5 将 Batch Norm拟合进神经网络（Fitting Batch Norm into a neural network）</p><p>3.6 为什么Batch Norm奏效？（Why does Batch Norm work?）</p><p>3.7 测试时的Batch Norm（Batch Norm at test time）</p><p>3.8 Softmax 回归（Softmax Regression）</p><p>3.9 训练一个Softmax 分类器（Training a softmax classifier） </p><p>3.10 深度学习框架（Deep learning frameworks） </p><p>3.11 TensorFlow（TensorFlow） </p><h2><a name='header-n90' class='md-header-anchor '></a>第三门课 结构化机器学习项目 (Structuring Machine Learning Projects) </h2><p><a href='lesson3-week1.md'>第一周：机器学习策略（1）(ML Strategy (1))</a> </p><p>1.1 为什么是ML策略？ (Why ML Strategy) </p><p>1.2 正交化(Orthogonalization) </p><p>1.3 单一数字评估指标(Single number evaluation metric) </p><p>1.4 满足和优化指标 (Satisficing and Optimizing metric)</p><p>1.5 训练集、开发集、测试集的划分(Train/dev/test distributions) </p><p>1.6 开发集和测试集的大小 (Size of the dev and test sets) </p><p>1.7 什么时候改变开发集/测试集和评估指标(When to change dev/test sets and metrics) </p><p>1.8 为什么是人的表现 (Why human-level performance?) </p><p>1.9 可避免偏差(Avoidable bias) </p><p>1.10 理解人类的表现 (Understanding human-level performance) </p><p>1.11 超过人类的表现(Surpassing human-level performance) </p><p>1.12 改善你的模型表现 (Improving your model performance) </p><p><a href='lesson3-week2.md'>第二周：机器学习策略（2）(ML Strategy (2))</a> </p><p>2.1 误差分析 (Carrying out error analysis) </p><p>2.2 清除标注错误的数据(Cleaning up incorrectly labeled data) </p><p>2.3 快速搭建你的第一个系统，并进行迭代(Build your first system quickly, then iterate) </p><p>2.4 使用来自不同分布的数据，进行训练和测试 (Training and testing on different distributions) </p><p>2.5 数据分布不匹配时，偏差与方差的分析 (Bias and Variance with mismatched data distributions) </p><p>2.6 处理数据不匹配问题(Addressing data mismatch) </p><p>2.7 迁移学习 (Transfer learning) </p><p>2.8 多任务学习(Multi-task learning) </p><p>2.9 什么是端到端的深度学习？ (What is end-to-end deep learning?) </p><p>2.10 是否使用端到端的深度学习方法 (Whether to use end-to-end deep learning) </p><h2><a name='header-n115' class='md-header-anchor '></a>第四门课 卷积神经网络（Convolutional Neural Networks）<span> </span></h2><p><a href='lesson4-week1.md'>第一周 卷积神经网络(Foundations of Convolutional Neural Networks)</a> </p><p>1.1<span> </span>计算机视觉（Computer vision）</p><p>1.2<span> </span>边缘检测示例（Edge detection example）</p><p>1.3<span> </span>更多边缘检测内容（More edge detection）</p><p>1.4<span> </span>Padding<span> </span></p><p>1.5<span> </span>卷积步长（Strided convolutions）<span> </span></p><p>1.6<span> </span>三维卷积（Convolutions over volumes）<span> </span></p><p>1.7<span> </span>单层卷积网络（One layer of a convolutional network）<span> </span></p><p>1.8<span> </span>简单卷积网络示例（A simple convolution network example）<span> </span></p><p>1.9<span> </span>池化层（Pooling layers）<span> </span></p><p>1.10 卷积神经网络示例（Convolutional neural network example）</p><p>1.11 为什么使用卷积？（Why convolutions?）</p><p><a href='lesson4-week2.md'>第二周 深度卷积网络：实例探究(Deep convolutional models: case studies)</a>
2.1 为什么要进行实例探究？（Why look at case studies?）</p><p>2.2 经典网络（Classic networks）</p><p>2.3 残差网络(ResNets)（Residual Networks (ResNets)）</p><p>2.4 残差网络为什么有用？（Why ResNets work?）<span> </span></p><p>2.5 网络中的网络以及 1×1 卷积（Network in Network and 1×1 convolutions）</p><p>2.6 谷歌 Inception 网络简介（Inception network motivation）<span> </span></p><p>2.7 Inception 网络（Inception network）<span> </span></p><p>2.8 使用开源的实现方案（Using open-source implementations）<span> </span></p><p>2.9 迁移学习（Transfer Learning）<span> </span></p><p>2.10 数据增强（Data augmentation）<span> </span></p><p>2.11 计算机视觉现状（The state of computer vision）<span> </span></p><p><a href='lesson4-week3.md'>第三周 目标检测（Object detection）</a>
3.1 目标定位（Object localization）</p><p>3.2 特征点检测（Landmark detection）</p><p>3.3 目标检测（Object detection）</p><p>3.4 滑动窗口的卷积实现（Convolutional implementation of sliding windows）</p><p>3.5 Bounding Box预测（Bounding box predictions）</p><p>3.6 交并比（Intersection over union）</p><p>3.7 非极大值抑制（Non-max suppression）</p><p>3.8 Anchor Boxes</p><p>3.9 YOLO 算法（Putting it together: YOLO algorithm）</p><p>3.10 候选区域（选修）（Region proposals (Optional)）</p><p><a href='lesson4-week4.md'>第四周 特殊应用：人脸识别和神经风格转换（Special applications: Face recognition &Neural style transfer）</a>
4.1 什么是人脸识别？(What is face recognition?)</p><p>4.2 One-Shot学习（One-shot learning）</p><p>4.3 Siamese 网络（Siamese network）</p><p>4.4 Triplet 损失（Triplet 损失）</p><p>4.5 人脸验证与二分类（Face verification and binary classification）</p><p>4.6 什么是神经风格迁移？（What is neural style transfer?）</p><p>4.7 CNN特征可视化（What are deep ConvNets learning?）</p><p>4.8 代价函数（Cost function）</p><p>4.9 内容代价函数（Content cost function）</p><p>4.10 风格代价函数（Style cost function）</p><p>4.11 一维到三维推广（1D and 3D generalizations of models）</p><h1><a name='header-n160' class='md-header-anchor '></a>第五门课 序列模型(Sequence Models)</h1><p><a href='lesson5-week1.md'>第一周 循环序列模型（Recurrent Neural Networks）</a>
1.1 为什么选择序列模型？（Why Sequence Models?）</p><p>1.2 数学符号（Notation）</p><p>1.3 循环神经网络模型（Recurrent Neural Network Model）</p><p>1.4 通过时间的反向传播（Backpropagation through time）</p><p>1.5 不同类型的循环神经网络（Different types of RNNs）</p><p>1.6 语言模型和序列生成（Language model and sequence generation）</p><p>1.7 对新序列采样（Sampling novel sequences）</p><p>1.8 循环神经网络的梯度消失（Vanishing gradients with RNNs）</p><p>1.9 GRU单元（Gated Recurrent Unit（GRU））</p><p>1.10 长短期记忆（LSTM（long short term memory）unit）</p><p>1.11 双向循环神经网络（Bidirectional RNN）</p><p>1.12 深层循环神经网络（Deep RNNs）</p><p><a href='lesson5-week2.md'>第二周 自然语言处理与词嵌入（Natural Language Processing and Word Embeddings）</a>
2.1 词汇表征（Word Representation）</p><p>2.2 使用词嵌入（Using Word Embeddings）</p><p>2.3 词嵌入的特性（Properties of Word Embeddings）</p><p>2.4 嵌入矩阵（Embedding Matrix）</p><p>2.5 学习词嵌入（Learning Word Embeddings）</p><p>2.6 Word2Vec</p><p>2.7 负采样（Negative Sampling）</p><p>2.8 GloVe 词向量（GloVe Word Vectors）</p><p>2.9 情绪分类（Sentiment Classification）</p><p>2.10 词嵌入除偏（Debiasing Word Embeddings）</p><p><a href='lesson5-week3.md'>第三周 序列模型和注意力机制（Sequence models & Attention mechanism）</a></p><p>3.1 基础模型（Basic Models）</p><p>3.2 选择最可能的句子（Picking the most likely sentence）</p><p>3.3 集束搜索（Beam Search）</p><p>3.4 改进集束搜索（Refinements to Beam Search）</p><p>3.5 集束搜索的误差分析（Error analysis in beam search）</p><p>3.6 Bleu 得分（选修）（Bleu Score (optional)）</p><p>3.7 注意力模型直观理解（Attention Model Intuition）</p><p>3.8注意力模型（Attention Model）</p><p>3.9语音识别（Speech recognition）</p><p>3.10触发字检测（Trigger Word Detection）</p><p>3.11结论和致谢（Conclusion and thank you）</p><p><strong>附件</strong></p><p><a href='interview.md'>人工智能大师访谈</a></p><p>吴恩达采访 Geoffery Hinton</p><p>吴恩达采访 Ian Goodfellow</p><p>吴恩达采访 Ruslan Salakhutdinov</p><p>吴恩达采访 Yoshua Bengio</p><p>吴恩达采访 林元庆</p><p>吴恩达采访 Pieter Abbeel<span> </span></p><p>吴恩达采访 Andrej Karpathy</p><p><a href='notation.md'>深度学习符号指南（原课程翻译）</a></p><p><a href='notation.md'>机器学习的数学基础</a></p><p> </p><p> </p></div>
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