Machine Learning

• approaching almost any ml problem
• ml with python, theory and implementation

Concepts:

• Learning rate scheduler (cosine)
• warmup
• Reduce LR on Plateau
• different optimizers
• Focal losses
• Exponential moving average(EMA)
• Restricted Boltzmann machine
• Hidden Markov Models (HMM), Conditional Random Fields (CRF),