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ML book

Introduction to ML

Overview
Linear regression
Linear classification
Evaluation Metrics
Decision Trees
Gradient boosting
Unsupervised Learning

Deep Learning

Multilayer perceptron (MLP)
CNN
Sequential NNs
- Vanilla RNN
- LSTM
- GRU
- Attention
- Transformers
Generative models
Reinforcement Learning

Python for ML

Python Basics
- Basic Types
- Variables
- Control flow
- Functions
- Classes
- ISLP Lab
NumPy
Pandas

Math for ML

Linear Algebra
Calculus
Probability
Statistics
- Maximum likelihood estimation (MLE)
Optimization

References

Notation
Bibliography
ML resources

.ipynb

Hyperparameters tuning

Contents

Grid search
Random search
Bayesian optimization

Hyperparameters tuning#

See here for a quick start.

Grid search#

Random search#

Bayesian optimization#

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Decision Trees

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