Machine Learning 文章导读

2024-09-04 21:26:08

Machine Learning Algorithms

Linear Regression and Gradient Descent

Local Weighted Regression Algorithm

Logistic Regression

Generative Model vs Discriminative Model

Naive Bayes and Laplace Smoothing

k-Nearest Neighbors Algorithm

Decision Tree Algorithm

Bootstrap,Bagging and Random Forest

SVM(1)：线性可分集的决策边界

SVM(2)：Lagrange Duality求解线性可分SVM的最佳边界

SVM(3)：Soft Margin 平衡之美

SVM(4)：SMO算法

Recommender System:

User-Based Collaborative Recommender System

Item-Based Collaborative Recommender System

Content-Based Recommender System

其它：

算法诊断及学习曲线

高斯辨别分析 Gaussian Discriminant Analysis

Overfit,Underfit and Regularization

逻辑回归求解二分类问题的Octave仿真

逻辑回归求解图像识别问题的Octave仿真

正规方程法求解多元线性回归的Octave仿真

基于高斯分布的异常检测算法

异常检测算法的Octave仿真

K-means Clustering

Neural Network and Deep Learning

Neural Network Basic

Parameter Initializations in Deep Learning

Feedforward Neural Network and BackPropagation Algorithm

Gradient Vanishing Problem in Deep Learning

L2 Regularization for Neural Networks

Cross-entropy Cost Function

Activation Functions and Their Derivatives

Optimization Algorithms

Gradient Descent(Batch/Stochastic/Mini-Batch)

Gradient Descent with Momentum and Nesterov Momentum

AdaGrad and RMSProp Algorithm

Adam Optimization Algorithm

Newton's method

Deep Learning

Grid Search for Tensorflow Deep Learning

Tensorflow(1):num_units in BasicLSTMCell

RNN(1): Architecture of Naive RNN

RNN(2): BPTT and Long-term Dependencies

RNN(3): LSTM and the Movie <Inside Out>

CNN(1): Architecture

CNN(2):Sparse Interactions, Receptive Field and Parameter Sharing

CNN(3):Convolution and Channels

CNN(4):Feature map size, Padding and Stride

CNN(5):Pooling Layer

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