DIFS - DO IT FROM SCRATCH

Everything I have learned, from data structures & algorithms to machine learning & robotics, is coded solely from scratch.

The bare mininum packages used here are just numerical toolboxes such as PyTorch or Numpy.

Codes are saved here publicly for my future review. Each package contains a README.md file that describes more details about things inside:

Traditional machine learning algorithms

• Linear regression (extend) => Compare regression with interpolation
• Logistic regression
• Decision tree
• Softmax regression
• k-Nearest Neighbor
• DBSCAN (Density-Based Spatial Clustering of Applications with Noise)
• Support vector machine

Deep learning

• Fully-connected network
• Convolutional network
• Long short-term memory
• Transformer

Statistical Signal Processing (SSP)

• Estimators
• Filters
• Hypothesis testing & classical inference algorithms
• Optimizers

TODO