人工智能1910期 全套视频+源码+课件
├─01_人工智能开发及远景介绍（预科）
│&nBSp;&nBSp;├┈1_何为机器学习.mp4
│&nBSp;&nBSp;├┈2_人工智能与机器学习关系.mp4
│&nBSp;&nBSp;├┈3_人工智能应用与价值.mp4
│&nBSp;&nBSp;├┈4_有监督机器学习训练流程.mp4
│&nBSp;&nBSp;├┈5_有监督机器学习训练流程.mp4
│&nBSp;&nBSp;├┈6_python机器学习库Scikit-Learn介绍.mp4
│&nBSp;&nBSp;└┈7_理解线性与回归.mp4
├─02_线性回归深入和代码实现
│&nBSp;&nBSp;├─01.视频
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈01_机器学习是什么_(new).mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈02_怎么做线性回归_(new).mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈03_理解回归_最大似然函数_(new).mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;└┈04_应用正太分布概率密度函数_对数总似然.mp4
│&nBSp;&nBSp;├─代码
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈linear_regression_0.py
│&nBSp;&nBSp;│&nBSp;&nBSp;└┈linear_regression_1.py
│&nBSp;&nBSp;├─软件
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈Anaconda3-4.2.0-Windows-X86_64.exe
│&nBSp;&nBSp;│&nBSp;&nBSp;└┈pycharm-cnmunity-2017.3.3.exe
│&nBSp;&nBSp;└─资料
│&nBSp;&nBSp;└─├┈机器学习是什么.txt
│&nBSp;&nBSp;└─└┈线性回归.txt
├─03_梯度下降和过拟合和归一化
│&nBSp;&nBSp;├─01.视频
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈01_梯度下降法思路_导函数有什么用.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈02_推导线性回归损失函数导函数_以及代码实现批量梯度下降.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈03_随机梯度下降_及代码实现_Mini-batchGD_调整学习率.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈04_梯度下降做归一化的必要性.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈05_最大值最小值归一化_sklearn官网介绍_防止过拟合W越少越小.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈06_过拟合的总结.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;└┈07_岭回归_以及代码调用.mp4
│&nBSp;&nBSp;├─代码
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈batch_grADient_descent.py
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈elastic_net.py
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈lasso_regression.py
│&nBSp;&nBSp;│&nBSp;&nBSp;└┈ridge_regression.py
│&nBSp;&nBSp;└─资料
│&nBSp;&nBSp;└─├┈过拟合.png
│&nBSp;&nBSp;└─└┈梯度下降法.txt
├─04_逻辑回归详解和应用
│&nBSp;&nBSp;├─01.视频
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈01_Lasso_ElasticNet_PolynomialFeatures.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈02_多项式回归代码_保险案例数据说明.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈03_相关系数_逻辑回归介绍.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈04_逻辑回归的损失函数_交叉熵_逻辑回归对比多元线性回归.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈05_逻辑回归sklearn处理鸢尾花数据集.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;└┈06_逻辑回归多分类转成多个二分类详解.mp4
│&nBSp;&nBSp;├─代码
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈elastic_net.py
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈INSurance.py
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈lasso_regression.py
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈logistic_regression.py
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈polynomial_regression.py
│&nBSp;&nBSp;│&nBSp;&nBSp;└┈ridge_regression.py
│&nBSp;&nBSp;└─资料
│&nBSp;&nBSp;└─├┈INSurance.csv
│&nBSp;&nBSp;└─├┈逻辑回归.txt
│&nBSp;&nBSp;└─├┈逻辑回归多分类.png
│&nBSp;&nBSp;└─└┈线性回归2.txt
├─05_分类器项目案例和神经网络算法
│&nBSp;&nBSp;├─01.视频
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈01_理解维度_音乐分类器数据介绍.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈02_傅里叶变化原理_傅里叶代码应用_傅里叶优缺点.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈03_逻辑回归训练音乐分类器代码_测试代码.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈04_人工神经网络开始.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈05_神经网络隐藏层的必要性.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;└┈06_神经网络案例_sklearn_concrete.mp4
│&nBSp;&nBSp;├─代码
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈logistic.py
│&nBSp;&nBSp;│&nBSp;&nBSp;└┈neural_network.py
│&nBSp;&nBSp;└─资料
│&nBSp;&nBSp;└─├┈concrete.csv
│&nBSp;&nBSp;└─├┈machine-learning.pdf
│&nBSp;&nBSp;└─├┈R04_神经网络.pdf
│&nBSp;&nBSp;└─├┈sine_a.wav
│&nBSp;&nBSp;└─├┈sine_b.wav
│&nBSp;&nBSp;└─├┈sine_mix.wav
│&nBSp;&nBSp;└─├┈trAInset.rar
│&nBSp;&nBSp;└─├┈理解维度_升维.png
│&nBSp;&nBSp;└─├┈神经网络.txt
│&nBSp;&nBSp;└─└┈图片1.png
├─06_多分类、决策树分类、随机森林分类
│&nBSp;&nBSp;├─01.视频
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈00_机器学习有监督无监督.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈01_逻辑回归多分类图示理解_逻辑回归和Softmax区别.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈02_Softmax图示详解_梯度下降法整体调参.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈03_评估指标_K折交叉验证.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈04_决策树介绍.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈05_随机森林_优缺点_对比逻辑回归_剪枝.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;└┈06_决策树_随机森林_sklearn代码调用.mp4
│&nBSp;&nBSp;├─代码
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈decision_tree_regressor.py
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈iris_bagging_tree.py
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈iris_decision_tree.py
│&nBSp;&nBSp;│&nBSp;&nBSp;└┈iris_random_forest.py
│&nBSp;&nBSp;└─资料
│&nBSp;&nBSp;└─├┈Softmax画图剖析.png
│&nBSp;&nBSp;└─├┈逻辑回归多分类画图剖析.png
│&nBSp;&nBSp;└─├┈逻辑回归二分类画图剖析.png
│&nBSp;&nBSp;└─├┈随机森林.pdf
│&nBSp;&nBSp;└─├┈梯度下降训练过程.png
│&nBSp;&nBSp;└─└┈线性回归(评估).pdf
├─07_分类评估、聚类
│&nBSp;&nBSp;├─01.视频
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈01_评估指标.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈02_监督学习评估指标代码调用.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈03_相似度测量.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈04_K-Means聚类.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;└┈05_KMeans聚类的应用.mp4
│&nBSp;&nBSp;├─代码
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈cluster_IMages.py
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈cluster_kmeans.py
│&nBSp;&nBSp;│&nBSp;&nBSp;└┈mnist.py
│&nBSp;&nBSp;└─资料
│&nBSp;&nBSp;└─├─test_data_home
│&nBSp;&nBSp;└─├┈flower2.png
│&nBSp;&nBSp;└─├┈Lena.png
│&nBSp;&nBSp;└─├┈temp_5.png
│&nBSp;&nBSp;└─└┈聚类.pdf
├─08_密度聚类、谱聚类
│&nBSp;&nBSp;├─01.视频
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈01_聚类的评估_metrics代码.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈02_密度聚类_代码实现.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;└┈03_谱聚类.mp4
│&nBSp;&nBSp;├─代码
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈cluster_DBSCAN.py
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈cluster_metrics.py
│&nBSp;&nBSp;│&nBSp;&nBSp;└┈cluster_spectral.py
│&nBSp;&nBSp;└─资料
├─09_深度学习、TensorFlow安装和实现线性回归
│&nBSp;&nBSp;├─01.视频
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈00_pIP安装源设置.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈01_TensorFlow介绍与安装.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈02_TensorFlow CUDA GPU安装说明_TF使用.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈03_TensorFlow代码初始_解析解多元线性回归实现.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;└┈04_tensorflow来代码实现线性回归_梯度下降优化.mp4
│&nBSp;&nBSp;├─代码
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈00_tensorflow_version.py
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈01_first_graph.py
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈02_better_session_run.py
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈03_global_variables_initializer.py
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈04_interactive_session.py
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈05_manager_graph.py
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈06_lifecycle.py
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈07_linear_regression.py
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈08_manually_grADients.py
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈09_autodiff.py
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈10_using_optIMizer.py
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈11_placeholder.py
│&nBSp;&nBSp;│&nBSp;&nBSp;└┈12_Softmax_regression.py
│&nBSp;&nBSp;└─资料
│&nBSp;&nBSp;└─└┈TensorFlow初识.pdf
├─10_TensorFlow深入、TensorBoard可视化
│&nBSp;&nBSp;├─01.视频
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈01_placeholder代码详解_TF构建Softmax回归计算图.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈02_TF对Softmax回归训练_评估代码实现.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈03_TF的模型持久化_重新加载.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;└┈04_模块化.mp4
│&nBSp;&nBSp;├─代码
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈11_placeholder.py
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈12_Softmax_regression.py
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈13_saving_model.py
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈14_restoring_model.py
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈15_modularity_.py
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈15_modularity.py
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈16_DNN.py
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈17_tensorboard.py
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈18_convolution.py
│&nBSp;&nBSp;│&nBSp;&nBSp;└┈19_pooling.py
│&nBSp;&nBSp;└─资料
│&nBSp;&nBSp;└─├─MNIST_data_bak
│&nBSp;&nBSp;└─└┈TensorFlow热恋.pdf
├─11_DNN深度神经网络手写图片识别
│&nBSp;&nBSp;├─01.视频
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈01_深度学习DNN是什么.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;└┈02_TF训练2层DNN来进行手写数字识别.mp4
│&nBSp;&nBSp;├─代码
│&nBSp;&nBSp;│&nBSp;&nBSp;└┈16_DNN.py
│&nBSp;&nBSp;└─资料
│&nBSp;&nBSp;└─├─MNIST_data_bak
│&nBSp;&nBSp;└─└┈TensorFlow热恋.pdf
├─12_TensorBoard可视化
│&nBSp;&nBSp;├─01.视频
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈01_TensorBoard代码.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;└┈02_TensorBoard启动以及页面.mp4
│&nBSp;&nBSp;└─代码
│&nBSp;&nBSp;└─└┈17_tensorboard.py
├─13_卷积神经网络、CNN识别图片
│&nBSp;&nBSp;├─01.视频
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈01_卷积1个通道的计算__垂直水平fiter图片.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈01_图释对比原始图片和卷积FeatureMap.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈02_三通道卷积_池化层的意思.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈03_CNN架构图LeNet5架构.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈04_CNN来对MNIST进行图片识别代码实现.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;└┈05_TF使用CNN来做CifaR10数据集分类任务.mp4
│&nBSp;&nBSp;├─代码
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈tensorflow_cnn_alexnet.py
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈tensorflow_cnn_cifaR10.py
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈tensorflow_cnn_mnist.py
│&nBSp;&nBSp;│&nBSp;&nBSp;└┈tensorflow_cnn_vgg.py
│&nBSp;&nBSp;└─资料
│&nBSp;&nBSp;└─└┈tutorials.rar
├─14_卷积神经网络深入、AlexNet模型实现
│&nBSp;&nBSp;├─01.视频
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈01_解决梯度消失的三个思路.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈02_反向传播计算W对应的梯度.mp4
│&nBSp;&nBSp;│&nBSp;&nBSp;└┈03_AlexNet五层卷积benchmark代码实现.mp4
│&nBSp;&nBSp;├─代码
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈tensorflow_cnn_alexnet.py
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈tensorflow_cnn_cifaR10.py
│&nBSp;&nBSp;│&nBSp;&nBSp;├┈tensorflow_cnn_mnist.py
│&nBSp;&nBSp;│&nBSp;&nBSp;└┈tensorflow_cnn_vgg.py
│&nBSp;&nBSp;└─资料
│&nBSp;&nBSp;└─├─test_data_home
│&nBSp;&nBSp;└─└┈TensorFlow热恋.pdf
└─15_Keras深度学习框架
└─├─01.视频
└─│&nBSp;&nBSp;├┈01_Keras开篇.mp4
└─│&nBSp;&nBSp;├┈02_Keras构建模型_Keras使用MNIST数据集训练CNN.mp4
└─│&nBSp;&nBSp;├┈03_Keras调用VGG16来训练.mp4
└─│&nBSp;&nBSp;└┈04_深度学习更种优化算法.mp4
└─├─代码
└─│&nBSp;&nBSp;├┈00_hello_keras.py
└─│&nBSp;&nBSp;├┈01_keras_model_sequential_.py
└─│&nBSp;&nBSp;├┈01_keras_model_sequential.py
└─│&nBSp;&nBSp;├┈02_keras_model_model.py
└─│&nBSp;&nBSp;├┈03_keras_mnist.py
└─│&nBSp;&nBSp;└┈04_keras_vgg16.py
└─└─资料
└─└─├─test_data_home
└─└─└┈TensorFlow热恋.pdf

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