It’s worth noticing that the more recent versions of pip can handle external dependencies using wheels, but, by using Anaconda, you’ll be able to install critical libraries for data science more smoothly. To circumvent this problem, Continuum Analytics released Anaconda, a Python distribution focused on scientific applications and Conda, a package and environment management system, which is used by the Anaconda distribution. However, for numerical computations, there are several dependencies that are not written in Python, so the initial releases of pip could not solve the problem by themselves. Since 2011, Python has included pip, a package management system used to install and manage software packages written in Python. Use the installed Python stack to build a neural network and train it to solve a classic classification problemįree Bonus: Click here to get access to a Conda cheat sheet with handy usage examples for managing your Python environment and packages.

See how to install the distribution on a Windows machine and use its tools to manage packages and environments.

Be introduced to Anaconda, a Python distribution proposed to circumvent these setup problems.

Walk through the details for setting up a Python environment for numerical computations on a Windows operating system.

It’s common for people to struggle to get things working in workshops involving the use of Python for machine learning, especially when they are using an operating system that lacks a package management system, such as Windows. However, to perform numerical computations in an efficient manner, Python relies on external libraries, sometimes implemented in other languages, such as the NumPy library, which is partly implemented using the Fortran language.ĭue to these dependencies, sometimes it isn’t trivial to set up an environment for numerical computations, linking all the necessary libraries. Python has been largely used for numerical and scientific applications in the last years.

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