Step 1 — install some useful dependencies

First, we install the numpy package, which provides support for large, multidimensional arrays and matrices as well as high-level mathematical functions. Then we install scipy, a library used for scientific computation. After that, it might be appropriate to install scikit-learn, a package considered the Python Swiss army knife for machine learning. In this case, we will use it for data exploration. Optionally, it could be useful to install pillow, a library useful for image processing, and h5py, a library useful for data serialization used by Keras for model saving. A single command line is enough for installing what is needed. Alternatively, one can install Anaconda Python, which will automatically install numpy, scipy, scikit-learn, h5py, pillow, and a lot of other libraries that are needed for scientific computing (for more information, refer to: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift, by S. Ioffe and C. Szegedy, arXiv.org/abs/1502.03167, 2015). You can find the packages available in Anaconda Python at https://docs.continuum.io/anaconda/pkg-docs. The following screenshot shows how to install the packages for our work: