Training Material | Addfor S.p.A.
The following IPython Notebooks are the standard training material distributed with the Addfor trainings. For more information about standard and custom training solutions please visit Training @ Addfor.
All the IPython notebooks are distributed under the Creative Commons Attribution-ShareAlike 4.0 International License.
We recommend to install the Anaconda distribution to the latest version: please visit continuum.io to download Anaconda. The tutorials work with python3 (python2 is no longer supported). After Anaconda installation update the distribution to the latest release:
conda update anaconda.
Clone this repository with git; use this command:
git clone --depth 1 https://github.com/addfor/tutorials if you want to download only the current commit (faster, takes less disk space):
Create a shallow clone with a history truncated to the specified number of commits.
NOTE: for Windows users, you can use this git client, or choose to download: click Clone or download and then Download ZIP (in this case skip the git clone step).
Next cd into tutorials and create the environment addfor_tutorials from the file
addfor_tutorials.yml (make sure the file is in your directory). Issue the command
conda env create -f addfor_tutorials.yml (the process could take few minutes). After the installation is finished, activate the environment:
activate myenvmacOS and Linux:
source activate myenv
All notebooks use our Addutils library: please install Addutils (for python3) before running the Notebooks. Download the zip file and open the Terminal or Anaconda Prompt:
source activate addfor_tutorials if environment is not already active, then type
pip install AddUtils-0.5.4-py34.zip (it should work for python3.4+).
At this point you are able to run the notebook with:
jupyter-notebook and navigate through the directory tree.
Note: the first time you run the notebooks you could experience a brief slowdown due to matplotlib building its font cache. It should disappear the next session.
For more informations visit: Download training material guidelines @ Addfor
- Python + IPython/Jupyter
- Machine learning
- Definitions and Advices
- Prepare the Data
- The scikit-learn interface
- Visualizing the Data
- Dealing with Bias and Variance
- Ensemble Methods
- Ensemble Methods Advanced
- Support vector machines (SVMs)
- Predict Temporal Series
- Forecasting with LSTM
- Prognostics using Autoencoder
- Theano Basic Concepts
- Explore Neural Network Hyperparameters with Theano and Keras
- Neural Networks with Nervana Neon library
- Tensorflow Basic concepts
- Explore Neural Network Hyperparameters with TensorFlow
- TensorFlow for beginners
- Keras - Theano Benchmark
- Neon Benchmark
- TensorFlow Benchmark
- Neural Network Benchmark Summary