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1.DNN 可设置网络结构参数,层数,神经元个数,激活函数 -------------------------------------------------------- 2.初始化: Random,"he","arXiv" 3.方差/偏差: Regularization, Dropout 4 随机采样: Stochastic gradient descent,mini-batch 5 优化方法: Gradient Descent,Momentum Gradient Descent,Adam 6 矩阵库更新: 主要考虑内存分配问题,但存在申请内存失败问题 -------------------------------------------------------------------- 1 深度神经网络: C++/python/TF 实现 2 卷积神经网络: python/TF 实现 3 序列模型: python/TF 实现 (RNN GRU LSTM) 4 DeepLearing学习笔记: 提纲 1神经网络构建 1.1神经网络基础 逻辑回归模型,Softmax,矩阵形式表示 1.2 神经网络的前向传递和反向传播的数学推导 线性,激活函数,损失函数,需要注意的是数据表示,维度问题 2 改善神经网络 2.1 防止过拟合 提前结束, 增加训练样本, 正则项, Dropout 2.2 梯度消失问题 初始化,梯度剪辑,激活函数, Batch-Norm,Res-net,LSTM 2.3 优化方法(加速 学习率) Mini-batch,动量的梯度下降,RMS-prop,Adam 2.4 梯度检验, 调参(网络结构:层,神经元个数;学习率: ;优化方法: ;正则化项: ;Dropout;Mini-batch size;迭代次数;初始化方式) 3 结构化机器学习 3.1 快速搭建好第一个系统:框架使用,开源使用, 迁移学习,数据预处理 3.2 误差分析:偏差和方差分析,正交化调试,控制变量法调试 1 偏差问题,调整网络架构,学习率,优化方法,增加数据集 2 方差问题(过拟合),正则化,Dropout,增加训练集 3 真实数据场景差 真实数据与训练测试数据分布不一致 3.3 单一评价标准,如F1值结合了精度和召回率 4 卷积神经网络 4.1 卷积,池化,填充,全连接概念以及超参数和参数(参数共享,特征提取**) 4.2 CNN构建的流程以及数学表示形式,需要注意的是,数据表示,维度问题 1 卷积,Relu,池化,向量化,全连接,Softmax层,前向传播 2 卷积,池化,全连接,Softmax层,链式求导 4.3 经典的CNN架构 LeNet-5,AlexNet,VGG(卷积核大小统一化),Resnet(解决梯度消失问题),Inception(可学习卷积核大小),迁移学习构建初始网络架构 5 序列模型 5.1 序列模型架构:many-to-many(实体识别,机器翻译), many-to-one(情感分类),one-to-many(语音生成),one-to-one 5.2 网络架构:RNN,GRU,LSTM,B-RNN,D-RNN 前向反向数学推导,需要注意的是数据表示,维度问题 5.3 Embeding:上下文关系学习词向量(*),skipgram,负采用,Glove词向量,去偏见,注意力机制(上下文加权)
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