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](http://shop.oreilly.com/product/0636920052289.do):
[![book](http://akamaicovers.oreilly.com/images/0636920052289/cat.gif)](http://shop.oreilly.com/product/0636920052289.do)
Simply open the [Jupyter](http://jupyter.org/) notebooks you are interested in:
* Using [jupyter.org's notebook viewer](http://nbviewer.jupyter.org/github/ageron/handson-ml/blob/master/index.ipynb)
* note: [github.com's notebook viewer](https://github.com/ageron/handson-ml/blob/master/index.ipynb) also works but it is slower and the math formulas are not displayed correctly,
* or by cloning this repository and running Jupyter locally. This option lets you play around with the code. In this case, follow the installation instructions below.
# Installation
First, you will need to install [git](https://git-scm.com/), if you don't have it already.
Next, clone this repository by opening a terminal and typing the following commands:
$ cd $HOME # or any other development directory you prefer
$ git clone https://github.com/ageron/handson-ml.git
$ cd handson-ml
If you want to go through chapter 16 on Reinforcement Learning, you will need to [install OpenAI gym](https://gym.openai.com/docs) and its dependencies for Atari simulations.
If you are familiar with Python and you know how to install Python libraries, go ahead and install the libraries listed in `requirements.txt` and jump to the [Starting Jupyter](#starting-jupyter) section. If you need detailed instructions, please read on.
## Python & Required Libraries
Of course, you obviously need Python. Python 2 is already preinstalled on most systems nowadays, and sometimes even Python 3. You can check which version(s) you have by typing the following commands:
$ python --version # for Python 2
$ python3 --version # for Python 3
Any Python 3 version should be fine, preferably ≥3.5. If you don't have Python 3, I recommend installing it (Python ≥2.6 should work, but it is deprecated so Python 3 is preferable). To do so, you have several options: on Windows or MacOSX, you can just download it from [python.org](https://www.python.org/downloads/). On MacOSX, you can alternatively use [MacPorts](https://www.macports.org/) or [Homebrew](https://brew.sh/). On Linux, unless you know what you are doing, you should use your system's packaging system. For example, on Debian or Ubuntu, type:
$ sudo apt-get update
$ sudo apt-get install python3
Another option is to download and install [Anaconda](https://www.continuum.io/downloads). This is a package that includes both Python and many scientific libraries. You should prefer the Python 3 version.
If you choose to use Anaconda, read the next section, or else jump to the [Using pip](#using-pip) section.
## Using Anaconda
When using Anaconda, you can optionally create an isolated Python environment dedicated to this project. This is recommended as it makes it possible to have a different environment for each project (e.g. one for this project), with potentially different libraries and library versions:
$ conda create -n mlbook python=3.5 anaconda
$ source activate mlbook
This creates a fresh Python 3.5 environment called `mlbook` (you can change the name if you want to), and it activates it. This environment contains all the scientific libraries that come with Anaconda. This includes all the libraries we will need (NumPy, Matplotlib, Pandas, Jupyter and a few others), except for TensorFlow, so let's install it:
$ conda install -n mlbook -c conda-forge tensorflow=1.0.0
This installs TensorFlow 1.0.0 in the `mlbook` environment (fetching it from the `conda-forge` repository). If you chose not to create an `mlbook` environment, then just remove the `-n mlbook` option.
Next, you can optionally install Jupyter extensions. These are useful to have nice tables of contents in the notebooks, but they are not required.
$ conda install -n mlbook -c conda-forge jupyter_contrib_nbextensions
You are all set! Next, jump to the [Starting Jupyter](#starting-jupyter) section.
## Using pip
If you are not using Anaconda, you need to install several scientific Python libraries that are necessary for this project, in particular NumPy, Matplotlib, Pandas, Jupyter and TensorFlow (and a few others). For this, you can either use Python's integrated packaging system, pip, or you may prefer to use your system's own packaging system (if available, e.g. on Linux, or on MacOSX when using MacPorts or Homebrew). The advantage of using pip is that it is easy to create multiple isolated Python environments with different libraries and different library versions (e.g. one environment for each project). The advantage of using your system's packaging system is that there is less risk of having conflicts between your Python libraries and your system's other packages. Since I have many projects with different library requirements, I prefer to use pip with isolated environments.
These are the commands you need to type in a terminal if you want to use pip to install the required libraries. Note: in all the following commands, if you chose to use Python 2 rather than Python 3, you must replace `pip3` with `pip`, and `python3` with `python`.
First you need to make sure you have the latest version of pip installed:
$ pip3 install --user --upgrade pip
The `--user` option will install the latest version of pip only for the current user. If you prefer to install it system wide (i.e. for all users), you must have administrator rights (e.g. use `sudo pip3` instead of `pip3` on Linux), and you should remove the `--user` option. The same is true of the command below that uses the `--user` option.
Next, you can optionally create an isolated environment. This is recommended as it makes it possible to have a different environment for each project (e.g. one for this project), with potentially very different libraries, and different versions:
$ pip3 install --user --upgrade virtualenv
$ virtualenv -p `which python3` env
This creates a new directory called `env` in the current directory, containing an isolated Python environment based on Python 3. If you installed multiple versions of Python 3 on your system, you can replace `` `which python3` `` with the path to the Python executable you prefer to use.
Now you must activate this environment. You will need to run this command every time you want to use this environment.
$ source ./env/bin/activate
Next, use pip to install the required python packages. If you are not using virtualenv, you should add the `--user` option (alternatively you could install the libraries system-wide, but this will probably require administrator rights, e.g. using `sudo pip3` instead of `pip3` on Linux).
$ pip3 install --upgrade -r requirements.txt
Great! You're all set, you just need to start Jupyter now.
## Starting Jupyter
If you want to use the Jupyter extensions (optional, they are mainly useful to have nice tables of contents), you first need to install them:
$ jupyter contrib nbextension install --user
Then you can activate an extension, such as the Table of Contents (2) extension:
$ jupyter nbextension enable toc2/main
Okay! You can now start Jupyter, simply type:
$ jupyter notebook
This should open up your browser, and you should see Jupyter's tree view, with the contents of the current directory. If your browser does not open automatically, visit [localhost:8888](http://localhost:8888/tree). Click on `index.ipynb` to get started!
Note: you can also visit [http://localhost:8888/nbextensions](http://localhost:8888/nbextensions) to activate and configure Jupyter extensions.
Congrats! You are ready to learn Machine Learning, hand
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handson-ml-master.zip (51个子文件)
handson-ml-master
index.ipynb 4KB
tools_matplotlib.ipynb 1.09MB
10_introduction_to_artificial_neural_networks.ipynb 321KB
02_end_to_end_machine_learning_project.ipynb 1.29MB
08_dimensionality_reduction.ipynb 3.55MB
06_decision_trees.ipynb 208KB
math_linear_algebra.ipynb 658KB
14_recurrent_neural_networks.ipynb 669KB
images
distributed
README 34B
deep
README 34B
dim_reduction
README 34B
decision_trees
README 34B
ann
README 34B
tensorflow
README 34B
ensembles
README 34B
end_to_end_project
california.png 10KB
README 34B
cnn
README 34B
test_image.png 178KB
svm
README 34B
training_linear_models
README 34B
autoencoders
README 34B
rnn
README 34B
classification
README 34B
fundamentals
README 34B
rl
README 34B
03_classification.ipynb 443KB
13_convolutional_neural_networks.ipynb 4.75MB
09_up_and_running_with_tensorflow.ipynb 230KB
16_reinforcement_learning.ipynb 1.25MB
requirements.txt 1KB
15_autoencoders.ipynb 341KB
datasets
lifesat
README.md 4KB
gdp_per_capita.csv 35KB
oecd_bli_2015.csv 396KB
inception
imagenet_class_names.txt 31KB
housing
housing.tgz 400KB
README.md 4KB
housing.csv 1.36MB
04_training_linear_models.ipynb 881KB
LICENSE 10KB
05_support_vector_machines.ipynb 897KB
book_equations.ipynb 48KB
README.md 8KB
01_the_machine_learning_landscape.ipynb 257KB
07_ensemble_learning_and_random_forests.ipynb 527KB
12_distributed_tensorflow.ipynb 17KB
11_deep_learning.ipynb 505KB
.gitignore 112B
tools_numpy.ipynb 620KB
tools_pandas.ipynb 395KB
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- vision_studio2018-01-04附书代码,谢谢分享
- qq_337890032018-01-14很不错的资料。
sakuralu
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