DataScience 学习笔记

Table of Contents





台湾国立大学 MLDS 笔记

  1. NON-Parametric methods
  2. lec-01 Regression case study
  3. lec-02 where dose error come from
  4. lec-03 Gradient descent
  5. lec-04 Classification
  6. lec-09 Recipe of Deep Learning
  7. lec-10 CNN
  8. lec-11 Why Deep
  9. lec-12 Semi-supervised Learning
  10. lec-13 Unsuprevised Learning
  11. lec-14 WordEmbedding
  12. lec-15 Neighbor Embedding
  13. lec-16 Auto-encoder
  14. lec-17 Deep Generative Model
  15. lec-19 Transfer Learning
  16. lec-20 SVM
  17. lec-21 Structure Learning
  18. lec-22 Structured Linear Model
  19. lec-23 Structured Support Vector Machine
  20. lec-24 Sequence Labeling Problem
  21. lec-24-2 Ensemble Learning
  22. lec-25 RNN
  23. lec-26 Recursive Network
  24. lec-27 Spatial Transformer Layer
  25. lec-28 Hightway Network and Grid LSTM
  26. lec-29 Language Modeling by RNN

斯坦福大学 CS20SI 笔记

  1. TFrecord 实用技术
  2. Tensorboard 可视化功能深入
  3. Tensorboard 的使用
  4. Tensorflow Debug 介绍
  5. Tensorflow inception-v3 图像识别
  6. Tensorflow inception-v3 迁移学习
  7. Tensorflow 加载和保存深入
  8. Tensorflow 基本概念
  9. Tensorflow 基本调参技巧
  10. Tensorflow 实现卷积神经网络
  11. Tensorflow 实现递归神经网络
  12. Tensorflow 改装 inception-v3
  13. Tensorflow 模型保存与加载
  14. Tensorflow 简单线性回归实例
  15. Tensorflow 网络运行
  16. tensorflow 实用技巧汇编
  17. 学习 Tensorflow nets 模块

加州伯克利 CS189 笔记

  1. lec 01 Introduction.
  2. lec 02 Classifiers
  3. lec 03 Perceptron Algorithm and Hard-SVM
  4. lec 04 Soft-SVM and Features
  5. lec 05 Hierarchical of ML and Optimization Problem
  6. lec 06 Decision theory
  7. lec 07 GAUSSIAN DISCRIMINANT ANALYSIS
  8. lec 08 EIGENVECTORS
  9. lec 09 ANISOTROPIC GAUSSIANS
  10. lec 10 REGRESSION aka Fitting Curves to Data
  11. lec 11.1 WEIGHTED LEAST-SQUARES REGRESSION
  12. lec 12 STATISTICAL JUSTIFICATIONS FOR REGRESSION
  13. lec 13 RIDGE REGRESSION
  14. lec 14 Kernel Perceptrons
  15. lec 15 DECISION TREES
  16. lec 16 DECISION TREES (continued)
  17. lec 17 NEURAL NETWORKS
  18. lec 18 NEURONS
  19. lec 19 Heuristics for Avoiding Bad Local Minima
  20. lec 20 UNSUPERVISED LEARNING
  21. lec 21 The Singular Value Decomposition (SVD)
  22. lec 22 SPECTRAL GRAPH CLUSTERING
  23. lec 23 Summary

Matplotlib 学习笔记

  1. Matplotlib basic concepts
  2. Matplotlib figures subplots and layouts
  3. Matplotlib plotting methods overview
  4. Matplotlib how to speak "MPL"
  5. Matplotlib limits legends and layouts
  6. Matplotlib MPL toolkits

NumpyScipy 学习笔记总结

  1. Numpy-1
  2. Numpy-2
  3. Scipy

Pandas 学习笔记总结

  1. 100 QandA of pandas
  2. Pandas-1
  3. Pandas-2
  4. Pandas-3

ScikitLearn 学习笔记总结

  1. 02.Scientific_Computing_Tools_in_Python
  2. 03.Data_Representation_for_Machine_Learning
  3. 100 QandA of sklearn
  4. Case Study - Text classification for SMS spam detection
  5. Case Study - Titanic Survival
  6. Cross-Validation and scoring methods
  7. Feature Selection
  8. In Depth - Decision Trees and Forests
  9. In Depth-Linear Models
  10. Methods - Text Feature Extraction with Bag-of-words
  11. Model Complexity and GridSearchCV
  12. Out-of-core Learning - Large scale Text Classification for sentiment Analysis
  13. Performance metrics and Model Evaluation
  14. Pipeline Estimators
  15. Review of scikit-learn API
  16. Supervised Learning Part1 – Classification
  17. Supervised Learning part-2: Regression
  18. Training and Testing Data
  19. Unsupervised Learning part 1 – Transformation
  20. Unsupervised Learning part 2 – clustering
  21. Unsupervised learning-Hierarchical and density-based clustering algorithms
  22. Unsupervised learning-anomaly detection
  23. Unsupervised learning-non-linear dimensionality reduction

Author: yiddishkop (yiddishkop@163.com)

Date:

Emacs 26.1 (Org mode 9.1.13)

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