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| images | ||
| nets | ||
| .gitignore | ||
| 01_the_machine_learning_landscape.ipynb | ||
| 02_end_to_end_machine_learning_project.ipynb | ||
| 03_classification.ipynb | ||
| 04_training_linear_models.ipynb | ||
| 05_support_vector_machines.ipynb | ||
| 06_decision_trees.ipynb | ||
| 07_ensemble_learning_and_random_forests.ipynb | ||
| 08_dimensionality_reduction.ipynb | ||
| 09_up_and_running_with_tensorflow.ipynb | ||
| 10_introduction_to_artificial_neural_networks.ipynb | ||
| 11_deep_learning.ipynb | ||
| 12_distributed_tensorflow.ipynb | ||
| 13_convolutional_neural_networks.ipynb | ||
| 14_recurrent_neural_networks.ipynb | ||
| 15_autoencoders.ipynb | ||
| 16_reinforcement_learning.ipynb | ||
| Dockerfile | ||
| Dockerfile-base | ||
| LICENSE | ||
| README.md | ||
| index.ipynb | ||
| math_linear_algebra.ipynb | ||
| requirements.txt | ||
| start-notebook.sh | ||
| tools_matplotlib.ipynb | ||
| tools_numpy.ipynb | ||
| tools_pandas.ipynb | ||
README.md
Machine Learning Notebooks
This project aims at teaching you the fundamentals of Machine Learning in python. It contains the example code and solutions to the exercises in my O'Reilly book Hands-on Machine Learning with Scikit-Learn and TensorFlow:
Simply open the Jupyter notebooks you are interested in:
- using Binder (recommended): launch binder
- no installation needed, you can immediately experiment with the code examples
- or using jupyter.org's notebook viewer
- note: github.com's notebook viewer also works but it is slower and the math formulas are not displayed correctly
- or by cloning this repository and running Jupyter locally
- if you prefer this option, follow the installation instructions below.
Installation
No installation is required, just click the launch binder button above, this creates a new VM with everything you need already preinstalled, so you'll be good to go in a just a few seconds! But if you insist, here's how to install these notebooks on your own system.
Obviously, you will need git and python (python 3 is recommended, but python 2 should work as well).
First, clone this repository:
$ cd {your development directory}
$ git clone https://github.com/ageron/handson-ml.git
$ cd handson-ml
If you want an isolated environment (recommended), you can use virtualenv:
$ virtualenv env
$ source ./env/bin/activate
There are different packages for TensorFlow, depending on your platform. Please edit requirements.txt and make sure only the right one for your platform is uncommented. Default is Python 3.5, Ubuntu/Linux 64-bits, CPU-only.
Also, if you want to go through chapter 16 on Reinforcement Learning, you will need to install OpenAI gym and its dependencies for Atari simulations.
Then make sure pip is up to date, and use it to install the required python packages:
$ pip install --upgrade pip
$ pip install --upgrade -r requirements.txt
If you prefer to use Anaconda, you can run the following commands instead:
$ conda install -c jjhelmus tensorflow=0.10.0
$ conda install -c conda-forge jupyter_contrib_nbextensions
If you want to install the Jupyter extensions, run the following command:
$ jupyter contrib nbextension install --user
Then you can activate an extension, such as the Table of Contents (2) extension:
$ jupyter nbextension enable toc2/main
Finally, launch Jupyter:
$ jupyter notebook
This should start the Jupyter server locally, and open your browser. If your browser does not open automatically, visit localhost:8888. Click on index.ipynb to get started. You can visit http://localhost:8888/nbextensions to activate and configure Jupyter extensions.
That's it! Have fun learning ML.