- Regression Algorithms
1.1 Linear Regression
1.2 Logistic Regression - Regularization Algorithms
2.1 Ridge Regression Regression
2.2 Lasso Regression
2.3 Elastic Net - Tree Based Models
3.1 Decision Tree
3.2 Random Forests
3.3 Lightgbm
3.4 XgBoost
3.5 Cat Boost
3.6 Gradient Boosting - Neural Networks and Deep Learning
4.1 Neural Networks
4.2 AutoEncoders
4.3 DeepLearning
4.4 Convolutional Neural Networks / CNN
4.5 Recurrent Neural Networks / RNN
4.6 LSTMs
4.7 GRUs
4.8 MxNet
4.9 ResNet
4.10 CapsuleNets
4.11 Unet
4.12 VGGs
4.13 Unet
4.14 Xception
4.15 Inception Nets
4.16 Computer Vision
4.17 Transfer Learning
4.18 RCNN
4.19 Object Detection
4.20 MobileNet - Clustering Algorithms
5.1 K Means Clustering
5.2 Hierarchial Clustering
5.3 DB Scan
5.4 Unsupervised Learning - Misc - Models
6.1 K Naive Bayes
6.2 SVMs
6.3 KNN
6.4 Recommendation Engine - DataMing Science
7.1 Data Science Techniques - Preprocessing
a. EDA, Exploration
b. Feature Engineering
c. Feature Selection
d. Outlier Treatment
e. Anomaly Detection
f. SMOTE
g. Pipeline
g. Missing Values
7.2 Data Science Techniques - Dimentionality Reduction
a. Dataset Decomposition
b. PCA
c. Tsne
d. SVD
7.3 Data Science Techniques - Post Modelling
a. Cross Validation
b. Model Selection
c. Model Tuning
d. Grid Search
7.4 Data Science Techniques - Ensemblling
a. Ensembling
b. Stacking
c. Bagging
d. Blending - Text Data
8.1. NLP
8.2. Topic Modelling
8.3. Word Embeddings
8.3. Spacy
8.4. NLTK
8.5. TextBlob - Data Science Tools
9.1 Scikit Learn
9.2 TensorFlow
9.3 Theano
9.4 Kears
9.5 PyTorch
9.6 Vopal Wabbit
9.7 ELI5
9.8 HyperOpt
9.9 Pandas
9.10 Sql
9.11 BigQuery
9.12 H2o
9.13 Fast.AI - Data Visualizations
10.1. Visualizations
10.2. Plotly
10.3. Seaborn
10.4. D3.Js
10.5. Bokeh
10.6. Highchart
10.7. Folium
10.8. ggPlot - Time Series
11.1. Time Series Analysis
11.2. ARIMA
11.3. Forecasting - Misc Materials
12.1. Best Tutorials on Kaggle
12.2. Data Leak
***待定
name | chapter | content | others |
---|---|---|---|
Great14 | 4 | [4] ResNet/CapsuleNetwork/VGG/InceptionNets | 0 |
chenzhuo | 4/9 | [4] 4.1/4.4/4.6/4.18;[9] 9.1/9.2 | 0 |
Jiede1 | 3 | 3.1~3.6 | 0 |
初期定为:每周交稿一次,个人提交代码+文档到各自分支,确认无误后提交合并merge请求,由Jiede1合并到master分支
更新条件:
1.算法的原理解释文档
2.算法示例的数据文件
3.算法代码