Table of Contents
快速掌握資料結構與演算法
- [2025-08-30] (Day 1) 介紹與準備
- [2025-08-31] (Day 2) 陣列 (Array)
- [2025-09-01] (Day 3) 矩陣 (Matrix)
- [2025-09-02] (Day 4) 鏈表 (Linked List)
- [2025-09-03] (Day 5) 堆疊 (Stack)
- [2025-09-04] (Day 6) 隊列 (Queue)
- [2025-09-05] (Day 7) 二元樹 (Binary Tree)
- [2025-09-06] (Day 8) 平衡樹 (Balanced Tree)
- [2025-09-07] (Day 9) 其他樹 (Other Trees)
- [2025-09-08] (Day 10) 圖 (Graph)
- [2025-09-09] (Day 11) 演算法評估 (Algorithm Analysis)
- [2025-09-10] (Day 12) 氣泡排序 (Bubble Sort)
- [2025-09-11] (Day 13) 選擇排序 (Selection Sort)
- [2025-09-12] (Day 14) 插入排序 (Insertion Sort)
- [2025-09-13] (Day 15) 基礎排序演算法比較
- [2025-09-14] (Day 16) 二元搜尋 (Binary Search)
- [2025-09-15] (Day 17) 內插搜尋 (Interpolation Search)
- [2025-09-16] (Day 18) 分治法 (Divide and Conquer)
- [2025-09-17] (Day 19) 動態規劃 (Dynamic Programming)
- [2025-09-18] (Day 20) 貪婪演算法 (Greedy Algorithm)
- [2025-09-19] (Day 21) 圖演算法 (Graph Algorithm)
- [2025-09-20] (Day 22) Dijkstra 最短路徑演算法 (Dijkstra’s Algorithm)
- [2025-09-21] (Day 23) Bellman-Ford 演算法
- [2025-09-22] (Day 24) Floyd-Warshall 演算法
- [2025-09-23] (Day 25) A* 搜尋演算法 (A-star Search)
- [2025-09-24] (Day 26) 最小生成樹 (Minimum Spanning Tree)
- [2025-09-25] (Day 27) Kruskal 演算法
- [2025-09-26] (Day 28) Prim 演算法
- [2025-09-27] (Day 29) 最小生成樹的實務應用 (Applications of MST)
- [2025-09-28] (Day 30) 系列結尾
30 天入門常見的機器學習演算法
- [2025-08-01] (Day 1) 介紹與準備
- [2025-08-02] (Day 2) 線性迴歸 (Linear Regression)
- [2025-08-03] (Day 3) 多項式迴歸 (Polynomial Regression)
- [2025-08-04] (Day 4) 正規化迴歸 (Regularization Regression)
- [2025-08-05] (Day 5) 邏輯迴歸 (Logistic Regression)
- [2025-08-06] (Day 6) 邏輯迴歸 (多項式 + 正規化)
- [2025-08-07] (Day 7) 回顧迴歸：從線性邏輯到學習本質
- [2025-08-08] (Day 8) K-近鄰 (K-Nearest Neighbors)
- [2025-08-09] (Day 9) 樸素貝氏分類器 (Naive Bayes Classifier)
- [2025-08-10] (Day 10) 支援向量機 (Support Vector Machine)
- [2025-08-11] (Day 11) 二元分類任務驗證指標
- [2025-08-12] (Day 12) 多元分類任務驗證指標
- [2025-08-13] (Day 13) 迴歸任務驗證指標
- [2025-08-14] (Day 14) 決策樹 (Decision Tree)
- [2025-08-15] (Day 15) 隨機森林 (Random Forest)
- [2025-08-16] (Day 16) K-Means
- [2025-08-17] (Day 17) 淺談深度學習 (Deep Learning)
- [2025-08-18] (Day 18) 全連接神經網絡 (Fully Connected Neural Network)
- [2025-08-19] (Day 19) 神經元 (Neuron)
- [2025-08-20] (Day 20) 激活函數 (Activation Function)
- [2025-08-21] (Day 21) 卷積神經網絡 (Convolutional Neural Network)
- [2025-08-22] (Day 22) 深度學習中的正規化與正則化 (Regularization in Deep Learning)
- [2025-08-23] (Day 23) 深度學習中的優化方法 (Optimization in Deep Learning)
- [2025-08-24] (Day 24) Adam 優化器 (Adaptive Moment Estimation)
- [2025-08-25] (Day 25) 循環神經網路 (Recurrent Neural Network)
- [2025-08-26] (Day 26) 長短期記憶 (Long Short-Term Memory)
- [2025-08-27] (Day 27) 閘控循環單元 (Gated Recurrent Unit)
- [2025-08-28] (Day 28) Seq2Seq (Encoder Decoder with RNN, LSTM, GRU)
- [2025-08-29] (Day 29) 注意力機制 (Attention Mechanism)
- [2025-08-30] (Day 30) 系列結尾