Machine Learning 文章导读

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

Regularization from Large Weights Perspective

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

Autoencoder