General Machine Learning / Stats / Techniques & Tips
- Choosing the right estimator: Scikit's Cheatsheet
- Deep NeuralNet : LSTM explanation
- Deep Learning for Computer Vision (Slides)
- Machine Learning Framework of Abhishek Thakur (Kaggle Grandmaster)
- CNN for 2D keypoint detection (with Lasagne)
- SoftMax vs Linear SVM
- Temporal-Difference Learning
- Intro to Boosting (AdaBoost)
- How DBScan works
- Visualising how DBScan splits data
- Decision Tree regression in action
- Impurity functions of Decision Tree
- Why averages suck and percentiles are great
Geometry
Measures
Time Series
Hidden Markov Model / Markov Chain
Statistical Model
- AAM, from theory to implementation
- PCA, why and how
- PCA, GPA, etc. thesis
- Procrustes Analysis
- Statistical Shape Models : Full PDF
- Statistical Shape Models : HTML
Optimisation / Linear Programming Techniques & Tips
- Gradient Descent: Best explanation ever
- How to build highly scalable recommendation system (Facebook)
- How to build recommendation system with distance measures
- Convex Optimisation, Textbook
- LBGFS
- Hungarian Algorithm
- Bayesian Optimisation
NLP Techniques & Tips
- Word2Vec: Explanation with visualisations
- Smith Waterman: Sequences alignment with visualisation
- Context-Free Grammar
Visualisation Techniques
Spark & Hadoop
- Create Hive table from HDFS file
- Sampling / Hypotheses testing in Spark
- Create custom Estimators/Transformers
- Trick to read Hive table from Impala (invalidate metadata first)