diff --git a/README.md b/README.md index 484e3a29..a18d75d1 100644 --- a/README.md +++ b/README.md @@ -49,28 +49,54 @@ Russian ISBN-13: 978-5970604090
## Table of Contents and Code Notebooks -Simply click on the `ipynb`/`nbviewer` links next to the chapter headlines to view the code examples (currently, the internal document links are only supported by the NbViewer version). +Simply click on the `ipynb`/`nbviewer`/[`Deepnote`](https://beta.deepnote.com) links next to the chapter headlines to view the code examples (currently, the internal document links are only supported by NbViewer and Deepnote). **Please note that these are just the code examples accompanying the book, which I uploaded for your convenience; be aware that these notebooks may not be useful without the formulae and descriptive text.** - Excerpts from the [Foreword](./docs/foreword_ro.pdf) and [Preface](./docs/preface_sr.pdf) -- [Instructions for setting up Python and the Jupiter Notebook](./code/ch01/README.md) +- [Instructions for setting up Python and the Jupiter Notebook](./code/ch01/README.md)
1. Machine Learning - Giving Computers the Ability to Learn from Data [[dir](./code/ch01)] [[ipynb](./code/ch01/ch01.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch01/ch01.ipynb)] + [](https://beta.deepnote.com/launch?template=data-science&url=https%3A%2F%2Fgithub.com%2Frasbt%2Fpython-machine-learning-book%2Fblob%2Fmaster%2Fcode%2Fch01%2Fch01.ipynb) + 2. Training Machine Learning Algorithms for Classification [[dir](./code/ch02)] [[ipynb](./code/ch02/ch02.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch02/ch02.ipynb)] + [](https://beta.deepnote.com/launch?template=data-science&url=https%3A%2F%2Fgithub.com%2Frasbt%2Fpython-machine-learning-book%2Fblob%2Fmaster%2Fcode%2Fch02%2Fch02.ipynb) + 3. A Tour of Machine Learning Classifiers Using Scikit-Learn [[dir](./code/ch03)] [[ipynb](./code/ch03/ch03.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch03/ch03.ipynb)] + [](https://beta.deepnote.com/launch?template=data-science&url=https%3A%2F%2Fgithub.com%2Frasbt%2Fpython-machine-learning-book%2Fblob%2Fmaster%2Fcode%2Fch03%2Fch03.ipynb) + 4. Building Good Training Sets – Data Pre-Processing [[dir](./code/ch04)] [[ipynb](./code/ch04/ch04.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch04/ch04.ipynb)] + [](https://beta.deepnote.com/launch?template=data-science&url=https%3A%2F%2Fgithub.com%2Frasbt%2Fpython-machine-learning-book%2Fblob%2Fmaster%2Fcode%2Fch04%2Fch04.ipynb) + 5. Compressing Data via Dimensionality Reduction [[dir](./code/ch05)] [[ipynb](./code/ch05/ch05.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch05/ch05.ipynb)] -6. Learning Best Practices for Model Evaluation and Hyperparameter Optimization [[dir](./code/ch06)] [[ipynb](./code/ch06/ch06.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch06/ch06.ipynb)] + [](https://beta.deepnote.com/launch?template=data-science&url=https%3A%2F%2Fgithub.com%2Frasbt%2Fpython-machine-learning-book%2Fblob%2Fmaster%2Fcode%2Fch05%2Fch05.ipynb) + +6. Learning Best Practices for Model Eval. and Hyperparameter Opt. [[dir](./code/ch06)] [[ipynb](./code/ch06/ch06.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch06/ch06.ipynb)] + [](https://beta.deepnote.com/launch?template=data-science&url=https%3A%2F%2Fgithub.com%2Frasbt%2Fpython-machine-learning-book%2Fblob%2Fmaster%2Fcode%2Fch06%2Fch06.ipynb) + 7. Combining Different Models for Ensemble Learning [[dir](./code/ch07)] [[ipynb](./code/ch07/ch07.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch07/ch07.ipynb)] + [](https://beta.deepnote.com/launch?template=data-science&url=https%3A%2F%2Fgithub.com%2Frasbt%2Fpython-machine-learning-book%2Fblob%2Fmaster%2Fcode%2Fch07%2Fch07.ipynb) + 8. Applying Machine Learning to Sentiment Analysis [[dir](./code/ch08)] [[ipynb](./code/ch08/ch08.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch08/ch08.ipynb)] + [](https://beta.deepnote.com/launch?template=data-science&url=https%3A%2F%2Fgithub.com%2Frasbt%2Fpython-machine-learning-book%2Fblob%2Fmaster%2Fcode%2Fch08%2Fch08.ipynb) + 9. Embedding a Machine Learning Model into a Web Application [[dir](./code/ch09)] [[ipynb](./code/ch09/ch09.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch09/ch09.ipynb)] + [](https://beta.deepnote.com/launch?template=data-science&url=https%3A%2F%2Fgithub.com%2Frasbt%2Fpython-machine-learning-book%2Fblob%2Fmaster%2Fcode%2Fch09%2Fch09.ipynb) + 10. Predicting Continuous Target Variables with Regression Analysis [[dir](./code/ch10)] [[ipynb](./code/ch10/ch10.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch10/ch10.ipynb)] + [](https://beta.deepnote.com/launch?template=data-science&url=https%3A%2F%2Fgithub.com%2Frasbt%2Fpython-machine-learning-book%2Fblob%2Fmaster%2Fcode%2Fch10%2Fch10.ipynb) + 11. Working with Unlabeled Data – Clustering Analysis [[dir](./code/ch11)] [[ipynb](./code/ch11/ch11.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch11/ch11.ipynb)] + [](https://beta.deepnote.com/launch?template=data-science&url=https%3A%2F%2Fgithub.com%2Frasbt%2Fpython-machine-learning-book%2Fblob%2Fmaster%2Fcode%2Fch11%2Fch11.ipynb) + 12. Training Artificial Neural Networks for Image Recognition [[dir](./code/ch12)] [[ipynb](./code/ch12/ch12.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch12/ch12.ipynb)] + [](https://beta.deepnote.com/launch?template=data-science&url=https%3A%2F%2Fgithub.com%2Frasbt%2Fpython-machine-learning-book%2Fblob%2Fmaster%2Fcode%2Fch12%2Fch12.ipynb) + 13. Parallelizing Neural Network Training via Theano [[dir](./code/ch13)] [[ipynb](./code/ch13/ch13.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch13/ch13.ipynb)] + [](https://beta.deepnote.com/launch?template=data-science&url=https%3A%2F%2Fgithub.com%2Frasbt%2Fpython-machine-learning-book%2Fblob%2Fmaster%2Fcode%2Fch13%2Fch13.ipynb) +
@@ -194,12 +220,24 @@ If you need help to decide whether this book is for you, check out some of the " ### Bonus Notebooks (not in the book) - Logistic Regression Implementation [[dir](./code/bonus)] [[ipynb](./code/bonus/logistic_regression.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/bonus/logistic_regression.ipynb)] + [](https://beta.deepnote.com/launch?template=data-science&url=https%3A%2F%2Fgithub.com%2Frasbt%2Fpython-machine-learning-book%2Fblob%2Fmaster%2Fcode%2Fbonus%2Flogistic_regression.ipynb) + - A Basic Pipeline and Grid Search Setup [[dir](./code/bonus)] [[ipynb](./code/bonus/svm_iris_pipeline_and_gridsearch.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/bonus/svm_iris_pipeline_and_gridsearch.ipynb)] + [](https://beta.deepnote.com/launch?template=data-science&url=https%3A%2F%2Fgithub.com%2Frasbt%2Fpython-machine-learning-book%2Fblob%2Fmaster%2Fcode%2Fbonus%2Fsvm_iris_pipeline_and_gridsearch.ipynb) + - An Extended Nested Cross-Validation Example [[dir](./code/bonus)] [[ipynb](./code/bonus/nested_cross_validation.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/bonus/nested_cross_validation.ipynb)] + [](https://beta.deepnote.com/launch?template=data-science&url=https%3A%2F%2Fgithub.com%2Frasbt%2Fpython-machine-learning-book%2Fblob%2Fmaster%2Fcode%2Fbonus%2Fnested_cross_validation.ipynb) + - A Simple Barebones Flask Webapp Template [[view directory](./code/bonus/flask_webapp_ex01)][[download as zip-file](https://github.com/rasbt/python-machine-learning-book/raw/master/code/bonus/flask_webapp_ex01/flask_webapp_ex01.zip)] - Reading handwritten digits from MNIST into NumPy arrays [[GitHub ipynb](./code/bonus/reading_mnist.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/bonus/reading_mnist.ipynb)] + [](https://beta.deepnote.com/launch?template=data-science&url=https%3A%2F%2Fgithub.com%2Frasbt%2Fpython-machine-learning-book%2Fblob%2Fmaster%2Fcode%2Fbonus%2Freading_mnist.ipynb) + - Scikit-learn Model Persistence using JSON [[GitHub ipynb](./code/bonus/scikit-model-to-json.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/bonus/scikit-model-to-json.ipynb)] + [](https://beta.deepnote.com/launch?template=data-science&url=https%3A%2F%2Fgithub.com%2Frasbt%2Fpython-machine-learning-book%2Fblob%2Fmaster%2Fcode%2Fbonus%2Fscikit-model-to-json.ipynb) + - Multinomial logistic regression / softmax regression [[GitHub ipynb](./code/bonus/softmax-regression.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/bonus/softmax-regression.ipynb)] + [](https://beta.deepnote.com/launch?template=data-science&url=https%3A%2F%2Fgithub.com%2Frasbt%2Fpython-machine-learning-book%2Fblob%2Fmaster%2Fcode%2Fbonus%2Fsoftmax-regression.ipynb) +