A few years ago, I was studying Machine Learning in school. In that time, I feel that playing Machine Learning is the best thing in the world, but what's the most unacceptable is that when it's applied to reality, it's much more complicated than I could think.
There are some contents for beginners:
Supervised Learning 監督學習
Unsupervised Learning 無監督學習
Reinforcement Learning 強化學習
Top 10 machine learning algorithms 10大機器學習算法
Decision tree 決策樹／判定樹
K-Means Clustering K –均值聚類
K-Nearest Neighbor Algorithm/KNN K近鄰算法
Support Vector Machine/SVM 支持向量機
Naive Bayes Classifier 樸素貝葉斯分類器
Gradient Boost 和 Adaboost 算法
Random Forest Algorithm 隨機森林算法
Neural Network 神經網絡
Markov Chains馬爾可夫鏈
Logistic Regression邏輯迴歸
數據集Data Set
訓練集 train set
驗證集validation set
測試集 test set
Training Models 訓練模型
Loss Function損失函數
Optimization Algorithms 優化算法
Gradient Descent Method 梯度下降法
Newtonian method 牛頓法
Momentum動量
Nesterov Momentum
Adagrad Adaptive Gradient
Adam Adaptive Moment Estimation
Estimate model 評估模型
Accuracy 準確率
Precision 精確率
Recall 召回率
True Positive Rate 真陽性率
Mean Square Error (MSE, RMSE) 平均方差
Absolute Error (MAE, RAE) 絕對誤差
The above is just the basic content about machine learning.
Stay hungry, Stay foolish !
感謝關注，謝謝
