Notes from the book Python Machine Learning with PyTorch and Scikit-Learn by Sebastian Raschka.
- The Perceptron (ch.2)
- Logistic regression (ch.3)
- Support vector machines (ch.3)
- Decision trees (ch.3)
- K-nearest neighbors (ch.3)
- Data pre-processing (ch.4)
- Dimensionality reduction (ch.5)
- Model evaluation and hyperparameters tuning (ch.6)
- Sentiment Analysis (ch.8)
- Linear regression (ch.9)
- Multilayer perceptron (ch.11)
- PyTorch (ch.12)
- PyTorch Computational Graph and Automatic Differentiation (ch.13)
- Convolutional Neural Networks (ch.14)
- EuroSAT classification (ch.14)
- Recurrent neural networks (ch.15)]
- Modeling Sequential Data (ch.15)
- Attention Mechanism (ch.16)