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My notes on some books I read on Machine Learning

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# My notes on some books I read on Machine Learning

- Hands-on Machine Learning with Scikit-Learn, Keras and TensorFlow - Aurélien Geron [[open notes]](hands-on-ml/notes.md)

- Python Machine Learning - Sebastian Rashcka [[open notes]](python-ml/notes.md)

- The Hundred-Page Machine Learning Book - Andriy Burkov [[open notes]](hundred-page-ml/notes.md)

- Introduction to Machine Learning with Python: A Guide for Data Scientists - Andreas C. Müller and Sarah Guido [[open notes]](intro-ml-python/notes.md)

- Building Machine Learning Powered Applications: Going from Idea to Product - Emmanuel Ameisen [[open notes]](build-ml-app/notes.md)

- Learning Spark: Lightning-Fast Data Analytics - Jules S. Damji, Brooke Wenig, Tathagata Das, Denny Lee [[open notes]](learning-spark/notes.md)

- An Introduction to Statistical Learning - Gareth M. James, Daniela Witten, Trevor Hastie, Robert Tibshirani [[open notes]](intro-statistical-learning/notes.md)

- Machine Learning Engineering - Andriy Burkov [[open notes]](ml-engineering/notes.md)

- Designing Machine Learning Systems - Chip Huyen [[open notes]](designing-ml-systems/notes.md)

# Applications

## WaveNet

From van den Oord's [paper](https://arxiv.org/abs/1609.03499)

![wavenet_gif](https://raw.githubusercontent.com/millengustavo/ml-books/master/images/wavenet.gif)

### Forecast Example

[Open jupyter notebook [ipynb]](./examples/time-series-LSTM-GRU-WaveNet.ipynb)

![wavenet_loss](https://raw.githubusercontent.com/millengustavo/ml-books/master/images/wavenet_loss.png)

![wavenet_forecast](https://raw.githubusercontent.com/millengustavo/ml-books/master/images/wavenet_forecast.png)

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